Edge AI chip startup Blaize has shown its first boards and tools for low latency, low power industrial designs.
The architecture developed by Blaize, which has design centres in Leeds and Kings Langley, UK, is optimized for running AI at the edge of the network. The Blaize Graph Streaming Processor (GSP) architecture has been implemented in a fully programmable chip with 16 cores. This delivers 16TOPS of AI inference performance for a power consumption of 7W.
The GSP chip has been used for a system-on-module (SoM) board and a PCI express card. These are programmed by a software development kit called Picasso or a code-free tool called AI Studio. The latency for an AI framework running on the chip is under 20ms. The chip can run five frameworks simultaneously with a total latency under 100ms.
“Today’s edge solutions are either too small to compute the load or too costly and too hard to productize,” said Dinakar Munagala, Co-founder and CEO, Blaize. “Blaize AI edge computing products overcome these limitations of power, complexity and cost to unleash the adoption of AI at the edge, facilitating the migration of AI computing out of the data centre to the edge.”
The P1600 SoM, shown above by Santiago Fernandez-Gomez, VP of Platform Engineering, is aimed at embedded systems and includes an ARM processor with video encode and decode as well as camera and USB interfaces so that no host processor is needed.
The Xplorer PCIe 3.0 boards are intended for an edge AI server or appliance. The X1600E is a small form factor accelerator platform for small and power-constrained environments such as convenience stores or industrial sites. The X1600P is a standard PCIe-based accelerator in a half-height, half-width form factor that can scale to eight boards that provide up to 64TOPS of AI inference performance.
Both the Picasso software development kit (SDK) and AI Studio tool use Blaize Netdeploy with edge-aware algorithms to get the best accuracy and performance for edge