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GC Nano User Interface (UI) Acceleration


Background and Overview

Crisp, clear, and responsive user interface HMI (human machine interface) has become equally important to the user experience as the content or the device form factor. A beautifully crafted smartphone that uses a combination of brushed titanium and smudge-proof glass may look great in the hand, but the user will quickly opt for another product if the user interface stutters or the screen is hard to read because of aliased and inconsistent fonts. The same scenario also applies to HMI in wearables and IoT devices, which is the focus of this white paper.

The goal of a well-designed wearable/IoT HMI is to make reading or glancing at the screen intuitive and natural, yet engaging. In other words, it is about a consistent, seamless interaction between user and device. Since device screens are smaller, information needs to be displayed in a simplified, uncluttered way with only relevant data (text, images, icons, video, etc.) rendered and composed onscreen. Smaller device screens do not directly translate into a device with less processing capabilities. The opposite can be true since upcoming devices need to perform real time processing (UI display composition, communications, sensor processing, analytics, etc.) as part of a single or network of IoT nodes. Some wearables/IoTs are taking technologies found in low/mid-range smartphone application processors and customizing parts of the IP specifically for wearables. One important IP that device OEMs need is the graphics processing unit (GPU) to accelerate HMI screen composition at ultra-low power.

In addition, a couple hot new trends in these emerging markets is personalized screen UI or a unified UI that spans all devices from cars and 4K TVs, to smartphones, wearables and embedded IoT screens to give users a consistent, ubiquitous screen experience across a given operating system (OS) platform, regardless of the underlying hardware (i.e. SoC/MCU). This will enable a cross vendor solution where vendor A’s smartwatch will work correctly with vendor B’s TV and vendor C’s smartphone. Google and Microsoft have recently announced support for these features in their Android Material Design and Windows 9 releases, respectively. Support for this requires an OpenGL ES 2.0 capable GPU at the minimum, with optional/advanced features using OpenGL ES 3.x. Google also has their light weight wearables OS called Android Wear that requires a GPU to give the UI a similar look-and-feel as their standard smartphone/tablet/TV Android OS.


Evolution of Wearables/IoT

Figure 1: Evolving Wearable and IoT Devices Requiring GPUs


Vivante GPU Product Overview

The underlying technology that accelerates HMI user experience is the graphics processing unit (GPU). GPUs natively do screen/UI composition including multi-layer blending from multiple sources (ISP/Camera, Video, etc.), image filtering, font rendering/acceleration, 3D effects (transition, perspective view, etc.) and lots more. Vivante has a complete top-to-bottom product line of GPU technologies that include the GC Vega and GC Nano Series:

  • GC Vega Series targets SoCs that need the latest and greatest GPU hardware and features like OpenGL ES 3.1, Full Android Extension Pack (AEP) Support including hardware tessellation / geometry shaders (TS/GS), DirectX 12, close to the metal GPU programming, hybrid ray tracing, zero driver overhead, sensor fusion, and GPU-Compute for vision processing using OpenVX, OpenCV or OpenCL, bundled in the most aggressive PPA and feature-complete design. Target markets range from high end wearables and low/mid-range mobile devices up to 4K TVs and GPUs for server virtualization.
  • GC Nano Series falls on the other side of the spectrum and targets devices that are making a revolutionary push into consumer products like wearables and IoT (smart homes / appliances, information gadgets, etc.) with GPU rendered HMI / UI. This core is specifically designed to work in resource constrained environments where CPU, memory (both on-chip and DDR), battery, and bandwidth are very limited. GC Nano is also optimized to work with MCU platforms for smaller form factors that require UI composition acceleration at 30/60+ FPS.


Vivante GPU Product Line and Markets

Figure 2: Vivante GPU Product Line and Target Markets


GC Nano Overview

GC Nano Series consists of the following products starting with the GC Nano Lite (entry), GC Nano (mainstream) and GC Nano Ultra (mid/high).



Figure 3: GC Nano Product Line


GC Nano Series benefits include:

  • Silicon Area and Power Optimized: Tiny silicon footprint that maximizes performance-per-area for silicon constrained SoCs means vendors can add enhanced graphics functionality to their designs without exceeding silicon/power budgets and still maintain responsive and smooth UI performance. GC Nano maximizes battery life with ultra-low power consumption and thermals with minimal dynamic power and near zero leakage power.
  • Smart Composition: Vivante’s Immediate Mode Rendering (IMR) architecture reduces composition bandwidth, latency, overhead and power by intelligently composing and updating only screen regions that change. Composition works either with GC Nano composing all screen layers (graphics, background, images, videos, text, etc.) or through a tightly coupled design where the GC Nano and display controller/processor (3rd party or Vivante DC core) work in tandem for UI composition. Data can also be compressed / decompressed through Vivante’s DEC compression IP core to further reduce bandwidth.
  • Wearables and IoT Ready: Ultra-lightweight vector graphics (GC Nano Lite) and OpenGL ES 2.0 (GC Nano, GC Nano Ultra) drivers, SDK and tools to easily transition wearables and IoT screens to consumer level graphical interfaces. The GCcNano 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/boot-up times and allows instant-on UI composition for screens that need to display information at the push of a button.
  • Ecosystem and Software Support: Developers can take advantage of the lightweight NanoUI or OpenGL ES API to further enhance or customize their solutions. Large industry support on existing Vivante products include the GC Nano / GC Nano 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. GC Nano helps 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 GC Nano Lite and the OpenGL ES 2.0 API (optional 3.x) 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 GC Nano 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 GC Nano innovations create a complete “visual” wearables MCU/SoC 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 experience. These new GPUs are making their way into some exciting products that will appear all around you as wearables and IoT get integrated into our lives.



Figure 4: GC Nano Series Features and Specifications


Figure 5: Example GC Nano Series SoC/MCU Implementation


Trends and Importance of 3D User Interface Rendering

In the UI sample in Figure 6 of a smart home device, next generation products will take some of the well thought out UI design elements from smartphones, tablets and TVs and incorporate them into IoT devices (and wearables) to keep a consistent interface between products. The similar UI look-and-feel will reduce usage learning curve and accelerate device adoption. As a side note, since different devices have different levels of processing/performance capabilities, a minimum level will be used for smaller screens (baseline performance) with additional features/higher performance added as device capabilities move up into a higher tier segmented by the OS vendor.



Figure 6: Sample HMI user interface on a smart home device


A few examples of updated UIs include the following:

  • Animated icons – easily shows the user which menu item is selected or where the input cursor is pointed to so the user does not need to spend time searching for cursor position onscreen. Icons can rotate, wiggle, pop out, flash, etc. before being selected.
  • Live animations – dynamic content can turn a simple background (wall paper) into a dynamic moving scene that can add a personal touch to your device. Background images and designs can also be personalized to match décor, lighting, theme and mood. Some white good appliance makers are testing these concept designs, hoping to put one (or two) inside your kitchen in the near future.
  • 3D effects – text, icons and images that go beyond simple shadows where feature of the GPU can render using powerful shader instructions to give 3-dimensional character to parts of the UI (ex. carousel, parallax, depth blur, widget/icon rendering to 3D/2D shapes, procedural/template animations for icon movements, physical simulations for particle systems, perspective view, etc.). These effects can be implemented using the GC Nano’s ultra-low power OpenGL ES 2.0/3.x pipeline.

GC Nano’s architecture excels at HMI UI composition by bringing out 3D UI effects, bandwidth reduction and reduced latency, which will be discussed below.


GC Nano Bandwidth Calculation

In this section we will step through examples of various user interface scenarios and calculate system bandwidth for both 30 and 60 FPS UI HMI rendering through the GC Nano GPU. All calculation assumptions are stated in section 5.2.

Methods of Composition

There are also two options for screen display composition that we will evaluate – first, where the GPU does the entire screen composition of all layers (or surfaces) including video and the display controller simply outputs the already composited HMI UI onscreen, and second, where the display controller takes composited layers from both GPU and video decoder (VPU) and does the final UI composition blend and merge before displaying. The top level diagrams below do not show DDR memory transactions, but they will be shown in section 5.2 when describing the UI steps.



Figure 7: GC Nano Full Composition: All UI layers are processed by GC Nano before sending the final output frame to the display controller


Figure 8: Display Controller Composition: Final output frame is composited by the display controller using input layers from GC Nano and the video processor


UI Bandwidth Calculations

Calculation assumptions:

  • GC Nano UI processing is in ARGB8 (32-bits per pixel) format. When GC Nano performs full composition, the GPU automatically converts 16-bit YUV video format into 32-bit ARGB.
  • Video frame is in YUV422 (16-bits per pixel) and has the same resolution as the screen size (GC Nano treats incoming video as video textures)
  • Final composited frame is in ARGB8 format (32-bits per pixel)
  • Reading video has a request burst size of 32-bytes
  • GC Nano UI request burst size is 64-bytes
  • Write sizes for writing out the UI rendering and final frame is 64-bytes
  • For these cases we assume 32-bit UI rendering. If the display format is 16-bits (applicable to smaller screens) then the bandwidth calculations listed below will be much lower.
  • Bandwidth calculation examples will be given for WVGA (800×480) and 720p (1280×720)
  • The amount of UI pixels per frame that need to be refreshed/updated (in our example) will include the following percentages:
    • 15% (standard UI)
    • 25%
    • 50% (worst case UI)


GC Nano Full UI Composition

The following images describe the flow of data to/from DDR memory using GC Nano to perform the entire UI composition. Some major benefits of using this method include using the GPU to perform some pre-post processing on images or videos, filtering, adding standard 3D effects to images/videos (video carousel, warping/dewarping, etc.) and augmented reality where GC Nano overlays rendered 3D content on top of a video stream. This method is the most flexible since the GC Nano can be programmed to perform image/UI related tasks.




Figure 9: GC Nano Full Composition memory access and UI rendering steps (steps 1 – 4)


Bandwidth calculation is as follows:



  • Total screen pixels = resolution WxH
  • UI pixels updated per frame = [Total screen pixels] * [UI% updated per frame]
  • Total UI pixels updated per frame in bytes = [UI pixels updated per frame] * [4 Bytes]; 4 Bytes since the UI format is 32bpp ARGB8888
  • Assumes video is in the background (worst case). Total composition Bandwidth (Bytes) = Video part [(a – c) * (2 Bytes for 16-bit YUV)] + UI part [c * 4 Bytes for ARGB8] + [a * 4 Bytes]
  • Total bandwidth per frame (MB) = [(c+d)/106]
  • Total bandwidth = [e*30] for 30 FPS and [e*60] for 60 FPS


Display Controller UI Composition

This section describes the flow of data to/from DDR memory using the display controller to do the final merging/composition of layers from the GC Nano and video processor. This method partially reduces bandwidth consumption since the GPU does not need to read in the video surface since it does not perform final frame composition. The GPU only works on composing the UI part of the frame minus any additional layers from other IP blocks inside the SoC/MCU. A benefit from this method is lower overall system bandwidth, but at the cost of less flexibility in the UI. If the video (or image) stream only needs be merged with the rest of the UI then this is a good solution. If the incoming video (or image) stream needs to be processed in any way – adding 3D effects, filtering, augmented reality, etc. – then this method has limitations and it is better to use the GPU for full frame UI composition.



Figure 10: Display controller performing final frame composition from two incoming layers from GC Nano and the video processor (VPU)


The display controller has a DMA engine that can read data from system memory directly. Data formats supported are flexible and include various ARGB, RGB, YUV 444/422/420, and their swizzle formats.

Bandwidth calculation for UI composition only is straightforward and is only based on the screen resolution size, as follows:




  • Total screen pixels = resolution WxH
  • Total UI pixels per frame = [Total screen pixels] * 4 Bytes; 32-bit ARGB8 format
  • Total bandwidth per frame (MB) = [b/106]; since GC Nano needs to perform full screen UI minus additional layers from other sources
  • Total bandwidth = [c*30] for 30 FPS and [c*60] for 60 FPS


Summary of Bandwidth Calculations

The table below summarizes the calculations above:



Adding Vivante’s DEC compression technology will also reduce bandwidth by about 2x – 3x from the numbers above.



GC Nano Architecture Advantage for UIs

There are two main architectures for GPU rendering, tile based rendering (TBR) and immediate mode rendering (IMR). TBR breaks a screen image into tiles and renders once all the relevant information is available for a full frame. In IMR graphics commands are issued directly to the GPU and executed immediately. Techniques inside Vivante’s architecture allows culling of hidden or unseen parts of the frame so execution, bandwidth, power, etc. is not wasted on rendering parts of the scene that will eventually be removed. Vivante’s IMR also has significant advantages when rendering photorealistic 3D images for the latest AAA rated games that take advantage of full hardware acceleration for fine geometries and PC level graphics quality, including support of advanced geometry/tessellation (GS/TS) shaders in its high end GC Vega cores (DirectX 11.x, OpenGL ES 3.1 and Android Extension Pack – AEP). Note: some of the more advanced features like GS/TS are not applicable to the GC Nano Series.

Tile Based Rendering (TBR) Architecture for UIs

The following images explain the process TBR architectures use for rendering UIs.

Breaking a scene into tiles…


But…before rendering a frame, all UI surfaces need to go through a pre-tile pass before proceeding…


Combining the pre-processing step and tiling step give us the following…


If the UI is dynamic then parts of the frame need to be re-processed…


Here are the “dirty” blocks inside the UI


TBR UI Rendering Summary

From the steps shown above, TBR based GPUs have additional overhead that increases UI rendering latency since the pre-processed UI triangles need to be stored in memory first and then read back when used. This affects overall frame rate. TBR GPUs also require large amounts of on-chip L2$ memory to store the entire frame (tile) database, but as UI complexity grows, either the on-chip L2$ cache size (die area) has to grow in conjunction or the TBR core has to continuously overflow to DDR memory which increases their latency, bandwidth and power.

TBRs have mechanisms to identify and track which parts of the UI (tiles) and which surfaces have changed to minimize pre-processing, but for newer UIs that have many moving parts; this continues to be a limitation. In addition, as screen sizes/resolutions and content complexity increases, this latency becomes even more apparent especially on Google, Microsoft, and other operating system platforms that will use unified UIs across all screens.

Immediate Mode Rendering (IMR) Architecture for UIs

The most advanced GPUs use IMR technology, which is object-based rendering found in PC (desktop/notebook) graphics cards all the way to Vivante’s GC Series product lines. IMR allows the GPU to render photorealistic images and draw the latest complex, dynamic and interactive content onscreen. In this architecture, graphics API calls are sent directly to the GPU and object rendering happens as soon as commands and data are received. This significantly speeds up 3D rendering performance.

In the case of UIs, the pre-pass processing is not required and this eliminates the TBR-related latency seen in section 6.1. In addition, there are many intelligent mechanisms that perform transaction elimination so hidden (unseen) parts of the frame are not even sent through the GPU pipeline, or if the hidden portions are already in-flight (ex. change in UI surface), those can be discarded immediately so the pipeline can continue executing useful work.

Composition processing is performed in the shaders for flexibility and the Vivante GPU can automatically add a rectangle primitive that takes the whole screen into account to achieve 100% efficiency (versus 50% efficiency using two triangles). Memory bandwidth is equivalent to TBR architectures for simple UIs and 3D frames, but for more advanced UIs and 3D scenes, TBR designs need to access external memory much more than IMR since TBRs cannot hold large amounts of complex scene data in their on-chip caches.

The following images explain the process Vivante’s IMR architecture uses for rendering dynamic UIs. The process is significantly simpler compared to TBRs, and dynamic changes in UI or graphics are straightforward.

IMR Object Based UI Rendering


Additional UI Content are Considered New Objects


IMR GPUs are Ideal for Dynamic and Next Gen UIs


IMR UI Rendering Summary

For dynamic 3D UIs, complex 3D graphics, mapping applications, etc., IMRs are more efficient in terms of latency, bandwidth and power. Memory consumption and memory I/O is another area where IMR has its advantages – for upcoming dynamic real time 3D UIs, IMR is the best choice and for standard UIs, IMRs and TBRs are equivalent but IMRs give the SoC/MCU flexibility and future-proofing. Note: historically, TBRs were better for simpler UIs and simple 3D games (low triangle/polygon count, low complexity) since TBRs could keep the full frame tile database on chip (L2$ cache), but advances in UI technologies brought about by leading smartphones, tablets and TVs have tipped things in favor of IMR technology.


GC Nano provides flexibility and advanced graphics and UI composition capabilities to SoCs/MCUs targeting IoT and wearables. With demand for high quality UIs that mirror other consumer devices from mobile to home entertainment and cars, a consistent, configurable interface is possible across all screens as the trend towards a unified platform is mandated by Google, Microsoft and others. GC Nano is also architected for OEMs and developers to take advantage of IMR technologies to create clean, amazing UIs that help product differentiation. The tiny core packs enough horsepower to take on the most demanding UIs at 60+ FPS in the smallest die area and power/thermal consumption. The GC Nano also reduces system bandwidth, latency, memory footprint and system/CPU overhead so resource constrained wearables and IoT SoCs/MCUs can use GPUs for next generation designs.

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.

Introducing Vega…the latest, most advanced GPUs from Vivante

By Benson Tao

Breaking News…

One of the latest headlines coming out of IDF 2013 in San Francisco today is the unveiling of a next generation GPU product line from Vivante. This technology continues to break through the the limits of size, performance, and power to help customers deliver unique products quickly and cost-effectively. The first generation solutions were introduced in 2007 (Generation 1) and upgraded again in 2010 (Generation 2) with new enhancements that were shipped in tens of millions of products. Gen 2 solutions already exceeded PC and console quality graphics rendering, which is the standard other GPU IP vendors strive to reach today. The next version (Gen 3) successfully hit key industry milestones by becoming the first GPU IP product line to pass OpenCL™ 1.1 conformance (CTS) and the first IP to be successfully designed into real time mission critical Compute applications for automotive (ADAS), computer vision, and security/surveillance. The early Gen 3 cores, designed and completed before the OpenGL ES 3.0 standard was fully ratified, were forward looking designs that have already passed OpenGL ES 3.0 conformance (CTS) and application testing. Many of the latest visually stunning games can be unleashed on the latest Gen 3 hardware found in leading devices like the Samsung Galaxy Tab 3 (7″), Huawei Ascend P6, Google Chromecast, GoogleTV 2.0/3/0, and other 4K TVs.

With the unveiling of Vivante’s fourth generation (codenamed “Vega”) ScalarMorphic architecture, the latest designs provide a foundation for Vivante’s newest series of low-power, high-performance, silicon-efficient GPU cores. Vivante engineering continues to respond quickly to industry developments and needs, and continuously refines and enhances its hardware specifications in order to remain at the top of the industry through partnerships with ecosystem vendors.

ProductsSample of Vivante Powered Products

What is Vega?

Vega is the latest, most advanced mobile GPU architecture from Vivante. Leveraging over seven years of architectural refinements and more than 100 successful mass market SOC designs, Vega is the cumulation of knowledge that blends high performance, full featured API support, ultra low power and programmability into a single, well defined product that changes the industry dynamics. SOC vendors can now double graphics performance and support the latest API standards like OpenGL ES 3.0 in the same silicon footprint as the previous generation OpenGL ES 2.0 products. Silicon vendors can also leverage the Vega design to achieve equivalent leading edge silicon process performance in a cost effective mainstream process. This effectively means that given the same SOC characteristics, a TSMC 40nm LP device can compete with a TSMC 28nm HPM version, at a more affordable cost that opens up the market to mainstream silicon vendors that were initially shut out of leading edge process fabrication due to their high initial costs.

Vega is also optimized for Google™ Android and Chrome products (but also supports Windows, BB OS and others), and fast forwards innovation by bringing tomorrow’s 3D and GPU Compute standards into today’s mass market products. Silicon proven to have the smallest die area footprint, graphics performance boost, and scalability across the entire product line, Vega cores extend Vivante’s current leadership in bringing all the latest standards to consumer electronics in the smallest silicon area. Vega 3D cores are adaptable to a wide variety of platforms from IoT (Internet-of-Things) and wearables, to smartphones, tablets, TV dongles, and 4K/8K TVs.

Whether you are looking for a tiny single shader stand-along 3D core or a powerhouse multi-core multi-shader GPU that can deliver high performance 3D and GPGPU functionality, Vivante has a market-proven solution ready to use. There are several options available when it comes to 3D GPU selection: 3D only cores, 3D cores designed with an integrated Composition Processing engine, and 3D cores with full GPGPU functionality that blend real-life graphics with GPU Compute. Vivante already is noted in the industry as the IP provider with the smallest, full-featured licensable cores in every GPU class.

Now let’s dive into some of the Vega listed features to see what they mean…

Hardware Features

  • ScalarMorphic™ architecture
    • Optimized for multi-GPU scalability and multi-threaded, multi-core heterogeneous platforms. This makes the GPU and GPU Compute cores as independent or cohesive as needed, flexible and developer friendly as new applications built on graphics + compute come online.
    • The same premium core architecture as previous generations is still intact, but it has been improved over time to remove inefficiencies. This also allows the same unified driver architecture to work with Vega cores and previous GC cores, so there is no waste of previous developer resources to re-code or overhaul apps for each successive Vivante GPU core.
    • Advanced scheduler and command dispatch unit for optimized shader load balancing and resource allocation.
    • Dynamic branching and non-constant varying indexing.
  • Ultra-threaded, unified shaders
    • Maximize graphics throughput, process millions of threads in parallel, and minimize latency.
    • The GPU scheduler and cores can process other threads while waiting for data to return from system memory, hiding latency and ensuring the cores are being used efficiently with minimal downtime. Context switching between threads is done automatically in hardware which costs zero cycles.
    • These shaders are more than just single way pipelines with added features that make the GPU more general purpose with multi-way pipelines to benefit various processing required for graphics and compute.
  • Patented math units that work in the Logarithmic space
    • In graphics there are different methods to calculate math and get the correct results.  With this method Vega cores can reduce area, power, and bandwidth that speeds up the overall system performance.
  • Fast, immediate hidden surface removal (HSR)
    • Eliminates render processing time by an average of 30% since a more advanced method to remove back-facing or obscure surfaces is implemented on the fly so minimal or no pre-processing time is wasted. This also goes beyond past versions where the GPU was automatically removing individual pixels (ex. early Z, HZ, etc.).
  • Power savings
    • Saves power up to 65% over previous GC Cores using intelligent DVFS and incremental low power architectural enhancements.
  • Proprietary Vega lossless compression
    • Reduces on-chip bandwidth by an average of 3.2:1 and streamlines the graphics subsystem including the GPU, composition co-processsor (CPC), interconnect, and memory and display subsystems. This is important to make sure the entire visual pipeline from when an app makes an API call to the output on the screen is smooth and crisp at optimal frame rates, with no artifacts or tearing regardless of the GPU loading.
  • Built-In Visual Intelligence
    • ClearView image quality – Life-like rendering with high definition detail, MSAA, and high dynamic range (HDR) color processing. This improves image quality, clarity, and matches real life colors that are not oversaturated.
    • Large display rendering – Up to 4K/8K screen resolution including multi-screen support that makes sure the GPU pipelines are balanced.
    • New additions using color correction can be implemented to correct color, increase color space using shaders (or OpenCL/RS-FS) or FRC.
    • NUIs can also take advantage of visual processing for motion and gesture.
  • Industry’s smallest graphics driver memory footprint
    • For the first time, smaller embedded or low end consumer devices and DDR-cost constrained systems can now support the latest graphics and various compute applications that fit those segments. With a smaller footprint you don’t need to increase system BOM cost by adding another memory chip, which is crucial in the cost sensitive markets.
    • There are also Vivante options that support DDR-less MCU/MPUs in the Vega series where no external DDR system memory exists.

More About the Shaders

  • Dynamic, reconfigurable shaders
    • Pipelined FP/INT double (64-bit), single/high (32-bit) and half precision/medium (16-bit) precision IEEE formats for GPU Compute and HDR graphics.
    • Multi-format support for flexibility when running compute in a heterogeneous architecture where coherency exists between CPU-GPU, high precision graphics, medium precision graphics, computational photography, and fast approximate calculations needed for fast, approximate calculations (for example, some image processing algorithms only need to approximate calculations for speed instead of accuracy). With these options, the GPU has full flexibility to target multiple applications.
    • High precision pipeline with support for long instructions.
  • Gigahertz Shaders
    • Updated pipeline enables shaders to run over 1 GHz, while lowering overall power consumption.
    • The high speed along with intelligent power management allows tasks to finish sooner and keep the GPU in a power savings state longer, so average power is reduced.
    • Cores scalable from tens of GFLOPS to over 1 TFLOP in various multi-core GPU versions.
  • Stream-Out Geometry Shaders
    • Increases on-chip GPU processing for realistic, HDR rendering with stream-out and multi-way pipelines.
    • The GPU is more independent when using GS since it can process, create and destroy vertices (and perform state changes) without taking CPU cycles. Previous versions required the CPU to pre-process and load states when creating vertices.

Application Programming Interface (API) Overview

Some of the APIs supported by Vega are listed below. This is not an exhaustive list but includes the key APIs in the industry and show the flexibility of the product line.

  • Full featured, native graphics API support includes:
    • Khronos OpenGL ES 3.0/2.0, OpenGL 3.x2.x, OpenVG 1.1, WebGL
    • Microsoft DirectX 11 (SM 3.0, Profile 9_3)
  • Full Featured, native Compute APIs and support:
    • Khronos OpenCL 1.2/1.1 Full Profile
    • Google Renderscript/Filterscript
    • Heterogeneous System Architecture (HSA)

Product Line Overview

Please visit the Vivante homepage to find more information on the Vega product line.

  • GC400L – Smallest OpenGL ES 2.0 Core – 0.8 mm2 in 28nm
  • GC880 – Smallest OpenGL ES 3.0 Core – 2.0 mm2 in 28nm
GC400 Series GC800 Series GC1000 Series GC2000 Series GC3000 Series GC4000 Series GC5000 Series GC6000 Series GC7000 Series
Vega-Lite Vega 1X Vega 2X Vega 4X Vega 8X
Core Clock in 28HPM (WC-125) MHz 400 400 800 800 800 800 800 800 800
Shader Clock in 28HPM (WC-125) MHz 400 800 1000 1000 1000 1000 1000 1000 1000
Pixel Rate
(GPixel/sec, no overdraw)
200 400 800 1600 1600 1600 1600 3200 6400
Triangle Rate
(M tri/sec)
40 80 123 267 267 267 267 533 1067
Vertex Rate
(M vtx/sec)
100 200 500 1000 1000 2000 2000 4000 8000
Shader Cores (Vec 4)
High/Medium Precision
1 1 2 4 4/8 8 8/16 16/32 32/64
High/Medium Precision
3.2 6.4 16 32 32/64 64 64/128 128/256 256/512
API Support
OpenGL ES 1.1/2.0
OpenGL ES 3.0 Optional Optional
OpenGL 2.x Desktop
OpenVG 1.1
OpenCL 1.2 Optional Optional
DirectX11 (9_3) SM3.0 Optional Optional
Key: ✓  (Supported)   – (Not supported)