Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Concept
Vision processing unit
Summary
A vision processing unit (VPU) is (as of 2023) an emerging class of microprocessor; it is a specific type of AI accelerator, designed to accelerate machine vision tasks.
Vision processing units are distinct from video processing units (which are specialised for video encoding and decoding) in their suitability for running machine vision algorithms such as CNN (convolutional neural networks), SIFT (scale-invariant feature transform) and similar.
They may include direct interfaces to take data from cameras (bypassing any off chip buffers), and have a greater emphasis on on-chip dataflow between many parallel execution units with scratchpad memory, like a manycore DSP. But, like video processing units, they may have a focus on low precision fixed point arithmetic for .
They are distinct from GPUs, which contain specialised hardware for rasterization and texture mapping (for 3D graphics), and whose memory architecture is optimised for manipulating in off-chip memory (reading textures, and modifying frame buffers, with random access patterns). VPUs are optimized for performance per watt, while GPUs mainly focus on absolute performance.
Target markets are robotics, the internet of things (IoT), new classes of digital cameras for virtual reality and augmented reality, smart cameras, and integrating machine vision acceleration into smartphones and other mobile devices.
Movidius Myriad X, which is the third-generation vision processing unit in the Myriad VPU line from Intel Corporation.
Movidius Myriad 2, which finds use in Google Project Tango, Google Clips and DJI drones
Pixel Visual Core (PVC), which is a fully programmable , Vision and AI processor for mobile devices
Microsoft HoloLens, which includes an accelerator referred to as a Holographic Processing Unit (complementary to its CPU and GPU), aimed at interpreting camera inputs, to accelerate environment tracking & vision for augmented reality applications.
Eyeriss, a design from MIT intended for running convolutional neural networks.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood