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AlphaICs Employs AI Special Agents

September 4, 2018

Author: Mike Demler

AlphaICs wants to create a new programming model for AI chips. The small Bangalore-based startup has devel­oped what it immodestly calls the Real AI Processor (Rap), which uses an array of special-purpose compute “agents.” In computer science, a multi-agent system com­prises a set of autonomous compute elements that processes inputs and communicates with each other to model com­plex be­ha­vior. Researchers use multi-agent models to study bio­logical, ecological, financial, and other complex systems.

In the Rap architecture, the agents are programmable tensor processors. The AlphaICs design runs agent programs by using a crossbar to dynamically connect Rap cores with a neural-network data path (NDP). These cores are RISC CPUs, and the NDP is an array of function blocks that compute NxN sums of products (SoPs), similar to a matrix multiply-accumulate (MAC) unit or tensor core.

Because agents run autonomously on the Rap processor, the company estimates a 10x latency reduction compared with CPU-controlled kernel programming frameworks such as OpenCV and OpenVX. According to its estimates, a 16nm implementation of the 32-agent processor has less than a 2ms latency for recurrent-neural-network (RNN) inferencing—a critical performance factor for autonomous vehicles and natural-language systems.

Having just $2.5 million in funding, AlphaICs needs a big cash infusion to offer a complete SoC, which will likely take at least two years to bring to production. On the other hand, licensing its core could produce immediate returns. To do so, the startup must compete against more-established IP providers, but the barrier to entry is much lower compared with ASICs.

Subscribers can view the full article in the Microprocessor Report.

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