Intel Vision Accelerator - Deep Learning in every home

Let us briefly dwell on the subject of new Intel processors (this is only a short while) and talk about machine vision and Deep Learning. In general, the topic of AI became common when discussing the prospects for the development of computer technology, and many, I think, noticed the following feature. Gradually, with the improvement of specialized hardware and software, AI elements are leaving the data centers with super-servers "into the fields", becoming more and more technically and financially accessible. Intel also sees this trend, and, in order to simplify the introduction of advanced technologies in everyday life, they offer vendors to take advantage of their new solution - Intel Vision Accelerator .

What is Intel Vision Accelerator? This is a set of reference designs for accelerator boards, on the basis of which any electronics manufacturer can create its product with the necessary set of features. However, naturally, just design is not enough - you need an element base. Intel already has it - this is a specialized Movidius and FPGA Arria coprocessor. What are the advantages of this approach?

• the possibility of inference of neural networks "in place";
• high performance on specialized tasks;
• high efficiency in terms of energy consumption, cost, etc .;
• full compatibility with the Open Visual Inference & Neural Network Optimization (OpenVINO) toolkit - a set of libraries, optimization tools and information resources for developing software using machine vision and Deep Learning.

Let's compare these two platforms with reference to Deep Learning.

	Intel Vision Accelerator with Intel Movidius	Intel Vision Accelerator with Intel Arria
Feature	High efficiency in terms of consumption and cost	Productive Integrated Solution
Application area	Classic neural networks	Additional optimization during deep learning using high-loaded networks
Use cases	Devices with limited size and consumption, classic network topologies that can be optimized for ASIC	Servers of the middle and initial levels, environments that respond well to software optimization
Connection Interfaces	PCIe, mini-PCIe, M2	PCIe
Number of video streams	From 1 to 16 per device	From 3 to 32
Batch size	1-4	1-144
power usage	~ 2 W	~ 35 W

Currently, Intel Vision Accelerator supports the following network topologies and algorithms:

Topologies	Algorithms
Googlenet ResNet – 18 ResNet – 50 ResNet – 101 SqueezeNet SqueezeNext VGG-16 Faster RCNN MobileNet Tiny yolo	Face detection / recognition Classification of face attributes Hand tracking Definition of gender and age Item Definition / Item Tracking Recognition of behavior and gestures Identification of abandoned items Multipurpose tracking Letter / word definition etc.

You may ask: why do we need to know about Intel offers for vendors? Manually, because accelerators are not offered. The answer is this: this is a post about a class of devices (or two classes) that will soon appear on the market. And you can find them for your needs - if you know what to look for.