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GCNano GPUs – Supercharging your Wearables and IoT

Vivante recently announced the GCNano GPU Series, the latest product line that complements its shipping Vega GC7000 Series to complete the world’s first full line-up of top to bottom GPU offerings, from the smallest wearables and IoT devices, to ultra HD 4K / 8K TVs, smartphones and tablets. GC7000 targets SoCs that need the latest and greatest GPU hardware and features like OpenGL ES 3.1, Full Android Extension Pack Support, DirectX 12, tessellation / geometry shaders, ray tracing, zero driver overhead and GPGPU for vision / image / physics processing, with the most aggressive PPA and feature-complete design. GCNano falls on the other side of the spectrum without sacrificing features or performance, and targets devices that are making a revolutionary (visual user interfaces / UI, network connected, intelligence) push into consumer products like wearables and IoT (smart homes / appliances, information gadgets, etc.). Many of these new products will be powered by microcontroller (MCU) and microprocessor (MPU) systems that will complement the general purpose applications processors (AP) found in mobile and home entertainment products. (I’ll use the term MCU to collectively reference MCUs and MPUs).

About MCUs

MCUs are task specific “embedded” processing devices that are found in the billions inside everything from the washing machine control / interface and thermostats with displays, to remote controls, network printers with displays, smart meters, and other devices in the home, car, infrastructure or on the body. Most of them are invisible to us, but behind the scene they keep our world moving. Traditional MCUs only supported basic visual interfaces since their focus was to display relevant information and keep things simple. Over the last several years the industry took a sharp turn and evolved to where MCUs where not just data processors but the HMI (human-machine interface) of some consumer devices. Nest thermostats, washing machines with color displays and the next generation health wearables are examples of this shift. As screens become pervasive, demand for fancier UIs that have an intuitive, consumer friendly look-and-feel will be required. GCNano specifically targets these types of systems since an innovative and special type of GPU needs to be used to overcome system and resource constraints, without negatively impacting user experience.

MCUs have limited CPU resources, limited memory resources, limited bandwidth, limited I/O, and require ultra low power (ex. long battery life). Previous products could get by using a simple display controller and CPU (software) or simple 2D engine to create a basic GUI. But as graphics UI complexity increases (layers, content, effects) and resolutions/PPI go up, this method will not suffice since it will overwhelm system resources. To overcome these limitations you cannot take a standard off the shelf GPU IP block and plug it in. You need to examine the constraints and optimize the design for this type of configuration through a holistic approach that includes hardware (GPU, UI-display controller integration), software (drivers, tools, compilers, etc.) and ecosystem enablement.

On the hardware side you are looking at the feature list and elminating unused ones (ex. 3D game features), optimizing PPA, fine-tuning datapaths and memory, enabling compression, reducing bandwidth and creating a tightly coupled interface between the UI / composition GPU and display controller. In addition, removing or significantly reducing external DDR memory cuts system cost dramatically since DDR is a major portion of BOM cost. On the software side you need to look at drastically cutting down driver size, driver overhead, batching calls, compilers (for example, pre-compiled shaders), and creating a standard wearables / IoT GPU SW package that developers can tap into. Having a tiny driver size is critical since you need instantaneous screen response at the push of a button (wearable) or when you start your car and the dashboard information needs to appear in less than a second (IoT). GPUs are complex, powerful and programmable, yet the GCNano takes a simpler approach and takes the guesswork out to keep things relevant and functional.

GCNano Product Overview

The GCNano can be split into two types of products. On one side you have the GCNano Lite which is a vector graphics engine that can render with no-OS and no-DDR memory and is shipping now (production proven). The other category is products that require 3D rendering using OpenGL ES 2.0 (at a minimum) but still need a tiny memory footprint (minimal DDR) and customized / limited / high-level operating systems (GCNano and GCNano Ultra). The table below shows the various products.

GCNano_slide_for_PR

GCNano Series benefits include:

  • Wearables and IoT Ready: Ultra-lightweight vector graphics (GCNano Lite) and OpenGL ES 2.0 (GCNano, GCNano Ultra) drivers, SDK and tools to easily transition wearables and IoT screens to consumer level graphical interfaces. The GCNano package also includes tutorials, sample code, and documentation to help developers optimize or port their code.
  • Designed for MCU/MPU Platforms: Efficient design to offload and significantly reduce system resources including complete UI / composition and display controller integration, minimal CPU overhead, DDR-less and flash memory only configurations, bandwidth modulation, close-to-the-metal GPU drivers, and wearables / IoT-specific GPU features to shrink silicon size. The tiny software code size puts less constraints on memory size, speeds up GPU initialization and boot-up times.
  • Ecosystem and Software Support: Developers can take advantage of the lightweight NanoUI or NanoGL API to further enhance or customize their solutions. Large industry support on existing Vivante products include the GCNano / GCNano Ultra product line on Android, Android Wear and embedded UI solutions from key partners covering tools for font, artwork and Qt development environments.
  • Compute Ready: As the number of wearable / IoT (processing) nodes grows by several tens of billions of units in the next few years, bandwidth on data networks could be an issue with an always-on, always-connected, always-processing node. GCNano products help with this by performing ultra low-power processing (GFLOP / GINT ops) at the node and only transmits useful compressed data as needed. Examples include sensor fusion calculations and image/video bandwidth reduction.

Vivante’s software driver stack, SDK and toolkit will support its NanoUI API that brings close-to-the-metal GPU acceleration for no-OS / no-DDR options on GCNano Lite and the NanoGL API for more advanced solutions that include proprietary or high-level operating systems like embedded Linux, Tizen™, Android™, Android™ Wear and other RTOS that require OpenGL ES 2.0+ in the smallest memory footprint. These various OS / non-OS platforms will form the base of next generation wearables and IoT that bring personalized, unique and optimized experiences to each person. The GCNano drivers include aggressive power savings, intelligent composition and rendering, and bandwidth modulation that allow OEMs and developers to build rich visual experiences on wearables and IoT using an ultralight UI / composition or 3D graphics driver.

Many of the GCNano innovations create a complete “visual” MCU platform that optimizes PPA and software efficiency to improve overall device performance and BOM cost, with the most compact UI graphics hardware and software footprint that does not diminish or restrict the onscreen user exprience. These new GPUs are making their way into some exciting products that will appear all around you as wearables and IoT get integrated and eventually “dissappear” into our lives.

Android Secure GPU Content Protection

Data and device security play a very important role in today’s always connected lifestyle. The proliferation of devices found at home, at work, in the car, and all around us (i.e IoT) blur the lines between personal, business, and our financial lives. We use our smartphones for ePayments, rent movies on our smart TV, and send secure corporate data from our tablets. While the exponential growth in the number of smart “personal/corporate” devices is great for convenience, it becomes a security nightmare with data explosion in the cloud and across our personal networks. We expect our confidential data to be protected and secure at all times, yet readily accessible anytime, anywhere.

In this blog we will take a different approach to data security and focus on our expertise, showing how to protect data as it travels down the visual processing pipeline through a secure GPU and memory subsystem inside an SoC.

Out of the Box Thinking

To make security even more secure, you need to think outside the box beyond 2-step authentication, encryption, network security, TrustZone, and others. There are great technologies that already cover those parts of security, so the solution developed by Vivante and the Android team takes another approach and is not intended to replace any of the existing protocols. The solution only augments the entire security process to give users even more peace of mind.

One area that we researched was how to keep data processed inside the GPU pipeline and rendered onscreen secure, through a tightly controlled GPU/SoC infrastructure. Historically, GPUs and security have never been used in the same sentence as synonymous technologies. GPUs were only focused on rendering life-like 3D graphics or performing Compute tasks for vision / image processing. Moving forward the industry will take a different approach to GPUs with the introduction of Vivante’s Secure GPU technologies, with the goal to bring GPU security to all mass market Vivante powered Android devices so everyone can benefit. Vivante already gained first-hand experience working on secure GPU solutions with some of our partners, one notable company being Blackberry (formerly known as Research in Motion), famous for their secure solutions used in enterprise and government offices, and other multimedia companies that used the GPU to process protected video content.

We looked for a solution that is truly usable, effective, scalable for future use cases, and economical, so we asked ourselves some questions like:

  • Is there any way to secure data to make sure it cannot be accessed by a rogue application?
  • What happens to the data that is in the frame buffer (surface) before, during, and after the rendering process? Can we ensure the data is safe and how?
  • Can we secure data for graphics, video, images, text, bitmaps, composition surfaces, and more?
  • How do you allocate surfaces and make sure secure surfaces reside in memory locations only accessible by secure clients only?
  • What happens in virtualization when a Vivante GPU can be virtualized (vGPU) across multiple users and multiple secure/non-secure operating systems (OS) and data needs to be firewalled?

All these questions were catalysts for the creation of a new Khronos EGL extension for the Android OS platform which will be highlighted below. There are parts of the Vivante GPUs architected for security and virtualization that go beyond what is described in this piece, and those will be described in a future blog.

Creation of a Secure GPU

Over the last several months, Vivante had been involved in deep technical discussions with Google’s Android team and Marvell to come up with a watertight solution to stop protected content from falling into the wrong hands. Protected content in this scenario relates to any visual confidential information like numbers, passwords, protected video, secure zones on the user interface (UI), GPU data in system memory and cache, and virtualization of the GPU that splits data between secure and non-secure (general) operating systems (OS).

The solution created is now the latest Khronos Group EGL extension that has become an important piece of the Android OS where they recommend its use. For reference, EGL (Embedded systems Graphics Library) is the main interface between client graphics APIs like OpenGL ES and the native windowing system. The EGL layer handles housekeeping functions like synchronization between threads, context management, memory allocation, binding/unbinding surfaces, and workload distribution between different rendering APIs.

Overview of the EGL Extension

In single and multi-OS environments, there are two types of memory/buffer accesses and copy functions, namely secure and non-secure. Non-secure accesses can only access non-secure surfaces/memory spaces while secure accesses can access both types based on a given platform/OS security policy. A non-secure access that tries to get data from a secure memory location is illegal, and the process will fail and result in program termination which is also flagged by the OS. Depending on the CPU/GPU, secure access violations may be different. Some architectures will cause a CPU exception while other systems will block read/writes and DMA transfers. A few platforms allow reads to get data (limited by system policy rules) but writes are completely blocked. There are also other methods for security exception handling not described here. All these failures will be system/OS dependent and must strictly adhere to the security guidelines. The EGL extension does not specify which method to implement, but it provides the mechanism to have secure/non-secure memory surfaces which can be used based on system policy.

To differentiate between the two new surface states, the extension adds a new attribute flag (EGL_PROTECTED_CONTENT_EXT) that indicates if it is secure (EGL_TRUE) or not (EGL_FALSE). EGL_TRUE (protected) surfaces reside in a secure memory region and non-protected surfaces are stored at all other non-protected memory locations. EGL and OpenGL ES (client API) allows data to be written to secure surfaces through protected and non-protected data, and also blocks secure surfaces from being referenced by non-secure devices/non-secure software in the system. Secure data cannot be written to non-secure surfaces and will terminate the process.

Copying content from one location to another also follows the same rules where secure surfaces cannot be copied to non-secure surfaces, but secure inter/intra-surfaces can copy data between locations. A non-secure to secure buffer transfer/access is possible, but this is left to system implementation.

Other parts of the specification take into account eglSwapBuffers and pbuffer surfaces. eglSwapBuffers is a function call that specifies the EGL surfaces to be swapped. eglSwapBuffers is allowed for secure surfaces if the windowing system can maintain the security of the buffer, and for non-secure surfaces, they can be accessed and copied to secure/non-secure areas.

pbuffers are pixel buffers that store non-visible pixels for off-screen rendering or to be used as a static resource that can be used by the shader (allocated once then de-allocated once program is complete). The security flag defines the state of the surface. If EGL_TRUE (secure) then protected surfaces can be written to by secure and non-secure accesses. Protected pbuffers cannot be accessed by non-secure devices and cannot be copied to non-protected memory, but can be copied to and from inter/intra protected surfaces. Non-protected surfaces can be accessed by both types of surfaces and copied to any other surface.

Other variations of manipulating, reading, writing, or copying/Blitting functions follow the same rules as above when it comes to protected/non-protected surfaces. Functions include, but are not limited to glReadPixels, glCopyTexImage2D, glCopyTexSubImage2D, glBlitFramebuffer, commit, allocate/Free, Lock/Unlock, etc.

Putting the Pieces Together

So how does the new EGL extension cover the following questions?

  • Is there any way to secure data to make sure it cannot be accessed by a rogue application?
    • Data buffers/surfaces will be either designated as secure (protected) or non-secure (non-protected) and rules apply to ensure secure data is only accessible to a select few applications that are designated as secure by the OS/platform vendor. Data can now be firewalled in conjunction with a protected memory management unit (MMU) that maps to both the GPU and CPU/system.

     

  • What happens to the data that is in the frame buffer (surface) before, during, and after the rendering process? Can we ensure the data is safe and how?
    • Data is always assigned to one category (protected or non-protected). There can be no switching of categories before, during, or after being processed by the GPU once allocated, or during memory transfers to/from system (GPU) memory. During the entire GPU data processing all the way to the display, the data is secure even during screen refreshes and after surfaces are de-allocated. After de-allocation the Vivante GPU performs data scrubbing functions to make sure the just used memory locations do not contain any relevant data. For additional security, the frame buffer contents can also be encrypted and compressed for safety and bandwidth/data reduction.

     

  • Can we secure data for graphics, video, images, text, bitmaps, composition surfaces, and more?
    • Yes, the GPU can process, manipulate, and secure each one of those surface types listed. The Vivante Composition Processing Core (CPC) can also help secure data alongside the 3D GPU.

     

  • How do you allocate surfaces and make sure secure surfaces reside in memory locations only accessible by secure clients only?
    • Surface allocation is assigned, pre-assigned, or can be allocated in real-time. During the allocation process the surface type is determined and lives with that surface until it is not used. Secure clients can access both types of surfaces while non-secure clients can only access non-secure surfaces.

     

  • What happens in virtualization when a Vivante GPU can be virtualized (vGPU) across multiple users and multiple secure/non-secure operating systems (OS) and data needs to be firewalled?
    • When the GPU is virtualized, we have a secure MMU (additional state bit) and secure bus interface with secure page tables that determine the type of surface and access patterns allowed. This allows the GPU core(s) to be virtualized across multiple OSes and applications and blocked off from each other. Secure transactions are allowed through a secure path and non-secure Read/Writes can go through the standard MMU and ACE-Lite/AXI interface. The whole process is strictly controlled by the platform and implemented in hardware.

More details about the EGL protected surface extension can be found here.

Huawei Ascend P6 Smartphone…Thin, Sexy, and Intuitive

By Benson Tao (Vivante Corporation)

The rise of Huawei is a corporate success story (and MBA case study) of determination and will to do whatever it takes to make a difference in people’s lives. This goal is achieved through the creation of the best and most innovative products possible without cutting corners or taking short cuts. This perseverance has turned Huawei from a tiny company that started selling PBX (private branch exchange or telephone switches) to a global behemoth that recently rose to take the crown as the world’s largest telecoms equipment maker in the world, surpassing Ericsson last year. The rapid rise of Huawei has also made it one of the top global brands in the world and a household name in some parts of the world. As the 2010 Fast Company fifth most innovative company in the world, a natural extension of their product line was to develop cutting edge smartphones and tablets to complement their existing user and communications infrastructure base. In the past year, their fruits of labor have helped push the mobile market forward with a few leading (and surprising) innovations:

  • Fastest Quad core smartphone (Ascend Quad D)
  • One of the first 1080p smartphones (Ascend Quad D2)
  • World’s fastest LTE smartphone (Ascend P2)
  • …AND the just announced thinnest smartphone in the world measuring only 6.18 mm thick (Ascend P6)

ascend-landing

Ascend P6 (Source: Huawei)

What do all these leading Huawei products have in common, other than all being branded under the Ascend name? At the heart of each product is one of the best architected Quad Core CPU and GPU combinations the market has seen, all packaged in a Hisilicon K3V2 SoC. Hisilicon is the semiconductor division of Huawei and is one of the suppliers of products to their systems division that defines or builds final products. Huawei can source product from Hisilicon, Qualcomm, and others to fit product requirements (cost, performance, low power, etc.).

One reason the K3V2 was chosen to power their flagship phones was the capability, scalability, extreme low power, and performance of the Vivante GC4000 (Graphics and Compute) GPU. Extensive due diligence to qualify the Vivante architecture was done under a microscope to make sure the GPU could meet all relevant claims. In the end, the GC4000 met or exceeded the demanding test criteria for 3D graphics performance to play the most intense and detailed mobile 3D games, and the Vivante CPC (Composition Processing Core) helped accelerate their intuitive Emotion UI user interface for responsive, smooth, and fluid feedback.

huaweip6

ascend-p6-teased-635

Source: Huawei

The Ascend P6 is the best and most beautiful smartphone Huawei has designed, with intricate details going into its amazing design (look), high quality materials, metallic body (feel), and ease of use. For more information about the Ascend P6, please visit their minisite.

The Importance of Graphics and GPGPU in ADAS and Other Automotive Applications

By Benson Tao (Vivante Corporation)

People spend a significant amount of time in their cars, whether commuting to work, going to the mall with the kids, or taking a road trip with loved ones. The car has evolved into an extension of our lives outside the home that blend driving fun with full featured electronics that give people a consumer device interface. In-car electronics have also moved beyond entertainment and fancy HMI (human-machine interface) displays to include intelligent safety monitoring and occupant protection systems. The automotive OEMs that build compelling consumer centric HMI / entertainment IVI (in-vehicle infotainment) and advanced safety features will be the ones driving higher automobile sales, and the best sellers will be the ones that create the most immersive in-car living room experience in a safe environment where the vehicle is “aware” of its surroundings. New GPU technologies enable automotive OEMs to realize both types of technologies with 3D graphics for visual eye candy and GPGPU (General Purpose Graphics Processing Unit) using OpenCL for safety applications. Upcoming vehicles shipped will have a predefined set of functions available on the HMI or IVI, but the car owner will be able to install different apps to customize the car interface. GENIVI (www.genivi.org) is an example of an open platform, industry consortium taking IVI to the next level.

Automotive Example

3D Graphics

3D graphics have been heavily used in the mobile market with the rapid expansion of Android and iOS. Each successive release of a mobile operating system or hardware technology pushes the visual envelope in terms of UI, 3D game play and captivating visual content. Since consumers are familiar with the look and feel of their existing mobile devices, the automotive market has taken note of this and started looking at in-vehicle platforms that display information in a similar manner. The first generation automobiles with embedded GPUs had basic graphics functionality which was limited in performance and capabilities since graphics was not an important requirement during the time that pre-dated the first iPhone shipments. Once the iPhone took off, adoption of GPU IP into system-on-chips (SOC) really took off and brought graphics into the spotlight where it could make-or-break a product. With this new paradigm shift, graphics proliferated into many important markets including the automotive industry where some SOC vendor designs are awarded based on the GPU inside.

Leveraging the mass market use of 3D graphics in mobile devices and building on the existing ecosystem around 3D graphics, dynamic and fancy UIs, and apps, automotive OEMs are using these building blocks to transform themselves from car makers to a new breed of consumer-focused automotive manufacturers that have the HW (car) and SW (apps, app store) to turn the driving experience upside down. One step to create this transition to their new business model is to bring the familiar graphical interfaces and user experiences found on tablets and TVs and transform the car into an entertainment hub powered through the IVI system and driver HMIs to add eye candy to console data displays. These displays need to scale to higher resolutions with higher DPI on HD screens that are crisp, clear, vivid, and responsive. The migration towards a visual-centric automobile console shows the importance of the GPU and how it has changed from a nice-to-have feature to a must-have requirement that sways technology decisions in the automotive ecosystem. The technology is available to put the pieces together in terms of hardware, software, middleware, and operating systems – it just comes down to putting the pieces together to make the final product and bringing the next generation graphics-centric solutions to a dealer near you, that goes beyond what is available today.

Infotainment 1

Safety Features

Safety is another major feature that influences purchase decisions. The term ADAS or Advanced Driver Assistance Systems describes the latest electronic technologies found in vehicles that focus on increasing safety for occupants, pedestrians, and surrounding vehicles. Features included in ADAS that monitor, predict, and try to prevent accidents include active safety monitoring, collision avoidance systems (CAS), object/pedestrian recognition, land departure warning, adaptive cruise control, and more. Current solutions use a combination of DSPs, CPUs, and in some cases FPGA with built-in computational units to perform safety monitoring. These solutions use hand written code for specific products, making them harder to port to new platforms or when changing components like DSPs or CPUs. With the GPU you can overcome these limitations by writing algorithms in OpenCL (described in more detail later) with some GPU based OpenCV libraries, and the code can be re-used across various platforms since it is cross platform compatible. In the near future, the compiler will be able to partition code to be executed on the most efficient compute element (GPU, CPU, DSP) in a platform to give the best overall performance. Parallel data will go to the GPU and serial data can go to the DSP or CPU.

Some automakers are looking at harnessing the massively parallel processing power of GPUs to reduce parallel algorithm execution times and speed-up real-time response in ADAS. Since the GPU is inherently fast at image and pixel processing, incoming pixel data from camera sensors and other sensors that are parallel in nature can be sent through the GPU to be processed. In addition GPGPU APIs like OpenCL can help process parallel data streams (sensor fusion) from cameras, GPS/WiFi positioning data, accelerometers, radar, and LIDAR to guide vehicles safely. Current solutions focus on computer vision (CV) as a first step, but moving forward data from other sensors can be sent to the GPU to offload other computational resources in a system. Autonomous cars like Google’s driverless car and those in DARPA competitions have already demonstrated what the future of ADAS will evolve into.

Entertainment and safety can be met with the latest semiconductor technologies like those found in the Freescale i.MX 6 automotive grade applications processors to enhance 3D/2D/VG graphics (HMI rendering, games, and user interface composition) and OpenCL (ADAS, computer vision, and gesture). So far the i.MX 6 is the only product that targets automotive with advanced graphics like OpenGL ES 3.0 and GPU compute with OpenCL 1.1.

MBZ ADAS_From Web

Source: Mercedes Benz

The Evolution of GPUs from Graphics to General Purpose Computation Cores (GPGPU)

The GPU was originally designed for 3D graphics applications and image rendering during the rasterization process. Over time the computational resources of modern graphics processing units became suitable for certain general parallel computations due to the massively parallel processing capabilities native to GPU architecture. Graphics is one of the best cases of parallel processing where the GPU needs to execute on billions of pixels or hundreds of millions of triangle vertices in parallel.

GPU architectures process independent vertices, primitives and fragments in great numbers using a large number of graphics shaders, which are also known as arithmetic logic units (ALUs) in the CPU world. Each primitive is processed the same way, using the same program or kernel. Many computational problems like image processing, analytics, mathematical calculations and others map well to this single-instruction-multiple-data (SIMD) architecture. The calculation speed-up shown and proven on SIMD processors was quickly recognized by researchers and developers and another area of high performance computing built on the vast processing power of GPUs was born. Today and in the near future, the fastest supercomputers and processing units use or will use GPU technology for the highest compute performance, calculation density, time savings, and overall system speed-up. The GPU has morphed from a graphics processor into a general purpose co-processor that sits alongside the CPU in today’s platforms.

The Penalties That Come With Less Than Optimal Graphical Processing

When selecting a GPU, there are certain requirements that need to be met when it comes to performance, power, and capabilities. Performance not only includes graphics benchmark results and 3D games, but also testing different applications that mirror real world use cases so the applications processor and GPU give the best overall user experience. As screen resolutions increase in both mobile devices and in-vehicle screens, the pixel count and triangle count (3D complexity) go up, leading to higher demand on the GPU as more objects need to be rendered onscreen. An underpowered GPU will lead to low performance (dropped frames, low FPS, image artifacts, incorrect rendering) and pretty much a non-usable device as evidenced by some of the first generation tablets that shipped but never used extensively. To get to the latest consumer electronics product levels, the GPUs in cars need to be upgraded from OpenGL ES 1.1 graphics to ES 2.0 and ES 3.0 capable cores with added shader performance to create eye catching visuals. i.MX 6 was one of the first SoCs where graphics was specifically defined at the product planning stages as Freescale had a vision of the car as a node in the internet of things (IOT) and graphics as the interface that couples man and machine. Content for cars (streamed media, social, games, apps) is also increasing as they become digitally connected with the rest of the consumer ecosystem. i.MX 6 is currently the only automotive SoC to support the latest APIs including OpenGL ES 3.0 and OpenCL 1.1. Other SoCs from Texas Instruments, Renesas, and FPGA based solutions have graphics capabilities, but rely on other solutions for OpenCL

The evolution of in-vehicle graphics went from an afterthought to a must have feature, migrating from simple onscreen text (that either used the CPU or simple 2D engine), to 2D graphics and then basic 3D. Today, there is another transition to advanced GPU rendering as seen on consumer devices that show detailed 3D models of your car in the console to highlight parts of the car in an easier to see format, 3D maps with street and building details, customizable/configurable HMI consoles similar to personalizing our Android smartphone, and much more. The initial solutions were underpowered but over time consumer expectations have grown to match their mobile devices going from a UI that was either scaled down or limited (ex. less icons, less menu layers, and basic 3D graphics) by specific hardware, to products that blur the line between consumer and auto.

According to Richard Robinson, principal analyst for automotive infotainment at iSuppli, “Infotainment hardware has undergone a rapid evolution during the last 13 years, moving from the traditional approach of dedicated hardware blocks, to the advent of bus-connected distributed architecture systems in the 2000 time frame, to the highly-integrated navigation-centric systems of 2006, to the new user-defined systems of today.”1

“The traditional boundaries between home, mobile and automotive infotainment systems are quickly going away. Consumers are now expecting the same features and equal access to their data across all these platforms,” said Jim Trent, VP and GM at NEC Electronics America2.

An Overview of OpenCL and Its Benefits.

OpenCL (Open Computing Language) is an open industry standard application programming interface (API) used to program multiple devices including GPUs, CPUs, as well as other devices organized as part of a single computational platform. The standard targets a wide range of devices from consumer electronics (smartphone, tablets, TVs) to embedded applications like automotive ADAS and computer vision (CV). Applications that already take advantage of the OpenCL performance speedup include medical imaging, video/image processing, high performance computing (HPC), robotics, surveillance, “Big Data” analytics, augmented reality, and gesture (motion, NUI). We will focus on the GPU aspect of OpenCL below.

The evolution of GPU computing has gone through a few major milestones. Pre-OpenCL, a program would be specifically written for and executed on a target device. This limited the features, performance, and calculation throughput to the device characteristics and there was not much flexibility beyond the hardware’s capabilities. The next step forward was the introduction of OpenCL where a hardware abstraction layer is created that separates the application from what is “under-the-hood” for ease of use and cross-platform portability. The abstraction layer queries all computational resources in a platform and uses them in the best way as a single cohesive unit to leverage as much computing horsepower as possible. Moving forward as we progress from OpenCL 1.1/1.2 to 2.0, advanced API features will be added along with making the solution even easier for general purpose programming.

At a high level, OpenCL provides both a programming language and a framework to enable parallel programming. The programming language is based on ISO C99 with math accuracy based on the IEEE 754 standard. OpenCL also includes libraries and a runtime system to assist and support software development. A developer can write general purpose OpenCL programs that executes directly on a GPU without needing to know 3D graphics or 3D APIs like OpenGL or DirectX. OpenCL also provides a low-level hardware abstraction layer as well as a framework that exposes many details of the underlying hardware layer allowing the programmer to take full advantage of it.

OpenCL uses the parallel execution SIMD (single instruction, multiple data) engines to enhance data computation density by performing massively parallel data processing on multiple data items, across multiple compute engines. Each compute unit has its own ALUs, including pipelined floating point (FP), integer (INT) units, and a special function unit (SFU) that can perform computations as well as transcendental operations. The parallel computations and associated series of operations is called a kernel, and the Vivante cores can execute millions of parallel kernels at any given time.

A Deeper Discussion of Graphics and OpenCL Benefits using Freescale’s i.MX 6 As An Example

Freescale uses GPU technology from a leading GPU IP provider based in Sunnyvale, California called Vivante to provide the 3D graphics, OpenVG, and OpenCL compute capability in their automotive grade i.MX 6 product line3. The i.MX6 applications processor is the industry’s first scalable, multicore ARM Cortex-A9 product line that spans single, dual, and quad core CPU architectures that are pin and software compatible. Integrated into the i.MX 6 is the GC2000 3D and OpenCL GPU, GC355 for fast hardware OpenVG acceleration, and the GC320 composition processing core (CPC) to compose screen content which the user sees. The applications processor also integrates the image processing unit (IPU) that accepts multiple camera input streams into the i.MX 6 for processing (ex. 360 degree view, rear view camera, and blind-spot detection).

The 3D graphics core provides 200 million triangles per second rendering horsepower which rivals performance of some of the latest tablets and smartphones, enabling the i.MX 6 to render ultra-realistic graphics and connect to app stores to play the latest games and display 3D UIs4. With this built-in capability and performance, ecosystem partners like QNX, Green Hills, Adeneo, Mentor Graphics, Rightware, Electrobit, and others are optimizing their operating systems, middleware, and applications to efficiently run the full feature set of the i.MX 6 GPU. The Freescale development platforms also have BSPs (board support packages) for Android and Linux to aid in the development of platforms in similar markets.

The OpenCL support currently focuses on accelerating Embedded (Computer) Vision applications that rely on camera inputs for ADAS. Some example applications where OEMs are analyzing GPU OpenCL performance are:

  • Feature Extraction – this is vital to many vision algorithms since image “interest points” and descriptors need to be created so the GPU knows what to process. SURF (Speeded Up Robust Features) and SIFT are examples of algorithms that can be parallelized effectively on the GPU. Object recognition and sign recognition are forms of this application.
  • Image filtering with different kernel sizes to enhance images.
  • Integral image for image acquisition can be spread across multiple i.MX 6 GPU shaders to cut down calculation time and parallelize execution.
  • Resampling – the GPU can use texture sampling to perform bilinear or bicubic filtering.
  • Point Cloud Processing – includes feature extraction to create 3D images to detect shapes and segment objects in a cluttered image. Uses could include adding augmented reality to street view maps.
  • Line detection – uses Hough Transform to detect lines in the input image (creates edge maps) followed by Sobel or Canny algorithms to further enhance edge detection. This can be used for lane detection
  • Pedestrian Detection – uses Histogram of Oriented Gradients (HOGS) to detect a person and automatically brake the car if the driver does not react in time.
  • Face recognition – goes through face landmark localization (exl Haar feature classifiers), face feature extraction, and face feature classification. Another use could be eye recognition to detect drowsiness and keep the vehicle within its lane.
  • Hand gesture recognition – separates hand from background (ex. color space conversion to the HSV color space) and then performs structural analysis on the hand to process motion.
  • Camera image de-warping – GPU performs matrix multiplications to map wide-angle camera inputs onto a flat screen so images are corrected. OEMs can use different camera vendors and to de-warp images they would only need to use different camera coefficients making the GPU easy to program.
  • Blind-spot detection – cameras can be used for blind spot detection using OpenCL to process stereo images. In this case, two cameras are needed per blind spot to detect depth so the GPU knows how far/close the other car is.

There applications listed above are examples of where OEMs are looking at using OpenCL on the GPU to speed up ADAS. There are many exciting

Background information: GPU vs. CPU for processing OpenCL

The best approach is to use a hybrid/heterogeneous platform (ex. HSA) to accelerate applications. CPU for task parallelism and serial computations & GPU for data parallel processing.

CPU vs GPU

Notes:

  1. http://www.isuppli.com/Automotive-Infotainment-and-Telematics/News/Pages/Automotive-Infotainment-Hardware-Enters-Fourth-Generation-Stage.aspx
  2. http://www.genivi.org/sites/default/files/press-releases/english/2009_11_10_GENIVI_Expands.pdf
  3. http://www.eetimes.com/electronics-news/4375956/Freescale-Vivante-Rightware-automotive-display
  4. http://www.cnx-software.com/2012/01/19/freescale-i-mx6-automotive-aerospace-infotainment-applications/

HSA Releases Ver. 0.95 of PRM Specification

By Benson Tao (Vivante Corporation)

HSAFoundation-Logo

Background

The Heterogeneous System Architecture (HSA) Foundation is a not-for-profit consortium that brings together some of the best minds (and companies) across the mobile, PC, consumer, HPC, Compute/Vision industries, along with leading academic institutions and anyone that wants to join in on the fun. The goal of HSA is to create a single architecture specification and standard programming interface (API) that developers can easily adopt to optimize distributed workloads across the GPU, CPU, DSP, and any other compute fabric element on the platform. From a high level view, the platform or system (with all the different components) can be viewed as one large, unified processor that executes a given workload. The main goal is to get the biggest bang for the buck or operational efficiency that includes the highest computational throughput (performance) at the lowest power and thermal envelope. Industry participants in HSA include SoC vendors, IP providers, OEMs, OSVs, and a full range of ISVs and application developers that want to make the best use of platform capabilities.

Vivante Contributes to Platform Innovation

Vivante joined HSA Foundation with the intention of pushing forward a defined specification that advances GPU Compute technologies in mobile, embedded, and consumer platforms. Many of our new and existing customers look to us for guidance on ways to improve their existing platforms and problems they are “stuck” on. Improvements can be as minor as performance gains, reduced BOM (or silicon) costs, and power savings, to re-architecting their designs (through GPU programmability) to fit new use cases and applications so they can extend product lifecycles without incurring major financial costs to replace/upgrade the existing infrastructure. These are some of the ways Vivante looks at defining solutions and future-proofing GPU/GPGPU IP cores to help its customers.

Vivante has multiple products targeting hybrid platforms from mass market cores that have the smallest silicon footprint with OpenGL ES 3.0 and OpenCL 1.1/RS-FS, to mid range and high performance multi-cluster configurable cores. The GPUs work directly with the CPU through a unified memory system, ACE-Lite™ cache coherency, or a native stream interface that connects directly to various compute fabrics. The Vivante HSA design, like the OpenGL ES graphics stack, supports a unified software and hardware package that provides a single architecture spanning multiple operating systems, platforms, and GPU cores. Vivante HSA software will also be backwards compatibility with all existing compute-enabled products and built around HSA APIs and tools that complement our current OpenCL™ and Google Renderscript™/Filterscript support. By simplifying the lives of application developers targeting heterogeneous architectures, programmers can create breakthrough use cases that take advantage of the new paradigm shift to hybrid computing. Real world applications that are already being accelerated by Vivante cores include computer vision, image processing, augmented reality, sensor fusion, and motion processing, with some examples being in the automotive ADAS sector (Advanced Driver Assistance Systems).

HSA Releases Ver. O.95 of the Highly Anticipated Programmers Reference Manual (PRM)

The fruits of hard labor of many technical discussions and architecture meetings over the last year since the consortium’s founding in June 2012 has finally come full circle with the release of version 0.95 of the PRM. This manual is a major milestone and lays the foundation for HSA to successfully move forward as it continues defining the platform of the future. The PRM also gives developers an early start as ecosystem partners create amazing applications, tools, libraries, and middleware programs that work best on HSA certified products.

Some features highlighted in the specification include:

1) Shared Coherent (Virtual) Memory Models

2) Atomics

3) User Mode and GPU Queuing

4) Zero Copy

5) Low Latency Dispatch

The specification also includes HSAIL (HSA Intermediate Language), which abstracts away from the native instruction set of the hardware and can be compiled automatically, in real-time, to the native ISA of the underlying hardware without any developer involvement. The same OpenCL and Renderscript/Filterscript programs can be abstracted and run on HSA platforms also.

Link to HSA Foundation website: http://hsafoundation.com/

Link to HSA Foundation press release: http://hsafoundation.com/hsa-foundation-announces-first-specification/