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The deep neural network accelerator based on the artificial intelligence processor SPR2801S is used in the field of high-performance edge computing and can be used as visual-based deep learning operation and AI algorithm acceleration. Universal USB interface for more convenient access to a variety of devices.

Features

– Support USB2.0 and 3.0 standard interface communication

– No programming needed, no language barriers

– Open SDK, can be applied to platforms such as X86, ARM, etc.

– Support Android, Linux and other operating systems

– Support VGG, SSD and other neural network models

Specification

NPU
Name	Lightspeeur SPR2801S(28nm process, unique MPE and ApiM architecture)
Energy efficiency	9.3 TOPs/Watt
Peak	5.6 Tops@100MHz
Low Power	2.8 Tops@300mW
Hardware interface	SDIO3.0 eMMC 4.5
Package	BGA(7mm*7mm)
Manufacturing process	28nm
USB accelerator
Size	66×19.5x10mm
Interface	USB 2.0,USB3.0 Type-A
Transmission Bandwidth	read bandwidth = 68.00 MB/s, write bandwidth = 84.69 MB/s
Working Voltag	DC 5V 200mA
Operation Temperature	0° C to 40° C
Storage Temperature	-20° C to 80° C
Framework	support Pytorch, Caffe framework, follow-up support TensorFlow
SDK Provided	ARM、X86 SDK
Tools	PLAI model traning tool(support for GG1,GNet18 and GNetfc network models based on VGG-16) Support Ubuntu, Windows operating system

About Lightspeeur® 2801S

Lightspeeur® 2801S is the world’s first commercially available deep learning CNN accelerator chip to run audio and video processing to power AI devices from Edge to Data Center.

Lightspeeur® pairs with a host processor to improve AI performance, while significantly reducing energy costs by minimizing host processing and power requirements with no extra memory requirements.

Lightspeeur® 2801S uses 100% proprietary and patented technologies to accelerate CNN processing at extremely high speeds, while consuming very little power.

GTI’s Matrix Processing Engine (MPE™) architecture is a multi-dimensional processing array of physical matrices of digital multiply-add (MAC) units that computes the series of matrix operations of a convolutional neural network. The scalable matrix design of the engines allows each engine to directly communicate and interact with adjacent engines, optimizing and accelerating data flow.

Application

– Edge computing
– Intelligent monitoring
– Smart toys and robots
– Smart home
– Virtual reality and augmented reality
– Face detection and recognition
– Speech Recognition
– Natural language processing
– Embedded deep learning device
– Cloud Machine Learning and Deep Learning System
– Artificial intelligence data center server
– Advanced assisted driving and autonomous driving