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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 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.

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