Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode

TL;DR

Vega is a highly efficient IoT end-node SoC with 10 cores, supporting ultra-low-power sleep modes and high-performance DNN acceleration, enabling advanced near-sensor analytics with long battery life.

Contribution

The paper introduces Vega, a novel 10-core RISC-V based SoC with integrated MRAM and ML accelerators, achieving state-of-the-art energy efficiency for IoT analytics.

Findings

Achieves 615 GOPS/W on 8-bit integer computation.

Supports 32.2 GOPS peak performance at 49.4 mW.

Provides ultra-low-power sleep mode of 1.7 μW.

Abstract

The Internet-of-Things requires end-nodes with ultra-low-power always-on capability for a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility to deal with complex and fast-evolving near-sensor analytics algorithms (NSAAs). We present Vega, an IoT end-node SoC capable of scaling from a 1.7 $\mathrm{\mu}$W fully retentive cognitive sleep mode up to 32.2 GOPS (@ 49.4 mW) peak performance on NSAAs, including mobile DNN inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile MRAM. To meet the performance and flexibility requirements of NSAAs, the SoC features 10 RISC-V cores: one core for SoC and IO management and a 9-cores cluster supporting multi-precision SIMD integer and floating-point computation. Vega achieves SoA-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3TOPS/W for 8-bit DNN inference with hardware acceleration). On floating-point (FP) compuation, it achieves SoA-leading efficiency of 79 and 129 GFLOPS/W on 32- and 16-bit FP, respectively. Two programmable machine-learning (ML) accelerators boost energy efficiency in cognitive sleep and active states, respectively.