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

• URL: http://arxiv.org/abs/2110.09101v1
• Date: Mon, 18 Oct 2021 08:47:45 GMT
• Title: Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode
• Authors: Davide Rossi, Francesco Conti, Manuel Eggimann, Alfio Di Mauro, Giuseppe Tagliavini, Stefan Mach, Marco Guermandi, Antonio Pullini, Igor Loi, Jie Chen, Eric Flamand, Luca Benini
• Abstract summary: Vega is an IoT end-node system capable of scaling from a 1.7 $mathrmmuW fully retentive cognitive sleep mode up to 32.2 GOPS (@ 49.4 mW) peak on NSAAs. Vega achieves SoA-leading efficiency of 615 GOPS/W on 8-bit INT and 79 and 129 GFLOPS/W on 32- and 16-bit FP.
• Score: 14.214500730272256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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