Accelerating ML Recommendation With Over 1,000 RISC-V/Tensor Processors on Esperanto's ET-SoC-1 Chip
David R. Ditzel
Abstract
Machine learning (ML) recommendation workloads have demanding performance and memory requirements and, to date, have largely been run on servers with x86 processors. To accelerate these workloads (and others), Esperanto Technologies has implemented over 1,000 low-power RISC-V processors on a single chip along with a distributed on-die memory system. The ET-SoC-1 chip is designed to compute at peak rates between 100 and 200 TOPS and to be able to run ML recommendation workloads while consuming less than 20 W of power. Preliminary data presented at the Hot Chips 33 Conference projected over a hundred times better performance per watt for an Esperanto-based accelerator card versus a standard server platform for the MLPerf Deep Learning Recommendation Model benchmark.