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Can I read Vega: A Ten-Core SoC for IoT Endnodes With DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode on EtoBox?
Vega: A Ten-Core SoC for IoT Endnodes With DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode by Davide Rossi; Francesco Conti; Manuel Eggiman; Alfio Di Mauro; Giuseppe Tagliavini; Stefan Mach; Marco Guermandi; Antonio Pullini; Igor Loi; Jie Chen; Eric Flamand; Luca Benini is a Engineering article available to read on EtoBox.
What is Vega: A Ten-Core SoC for IoT Endnodes With DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode about?
The Internet-of-Things (IoT) requires endnodes 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 endnode system on chip (SoC) capable of scaling from a 1.7-μW fully retentive cognitive sleep mode up to 32.2-GOPS (at 49.4 mW) peak performance on NSAAs, including mobile deep neural network (DNN) inference, exploiting 1.6 MB of state-retentive SRAM, and 4 MB of non-volatile magnetoresistive random access memory (MRAM). To meet the performance and flexibility requirements of NSAAs, the SoC features ten RISC-V cores: one core for SoC and IO management and a nine-core cluster supporting multi-precision single instruction multiple data (SIMD) integer and floating-point (FP) computation. Vega achieves the state-of-the-art (SoA)-leading efficiency of 615 GOPS/W on 8-bit INT computation (boosted to 1.3 TOPS/W for 8-bit DNN inference with hardware acceleration). On FP computation, it achieves the SoA-leading efficiency of 79 and 129 GFLOPS/W on 32-and 16-bit FP, respectively. Two programmable machi
Who reads Vega: A Ten-Core SoC for IoT Endnodes With DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Davide Rossi; Francesco Conti; Manuel Eggiman; Alfio Di Mauro; Giuseppe Tagliavini; Stefan Mach; Marco Guermandi; Antonio Pullini; Igor Loi; Jie Chen; Eric Flamand; Luca Benini
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Published
- 2022
- Language
- EN
- Field
- Engineering (Physical Sciences)