Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing
Metadata only
Datum
2019-07Typ
- Journal Article
Abstract
This paper presents Mr. Wolf, a parallel ultra-low power (PULP) system on chip (SoC) featuring a hierarchical architecture with a small (12 kgates) microcontroller (MCU) class RISC-V core augmented with an autonomous IO subsystem for efficient data transfer from a wide set of peripherals. The small core can offload compute-intensive kernels to an eight-core floating-point capable of processing engine available on demand. The proposed SoC, implemented in a 40-nm LP CMOS technology, features a 108-mu W fully retentive memory (512 kB). The IO subsystem is capable of transferring up to 1.6 Gbit/s from external devices to the memory in less than 2.5 mW. The eight-core compute cluster achieves a peak performance of 850 million of 32-bit integer multiply and accumulate per second (MMAC/s) and 500 million of 32-bit floating-point multiply and accumulate per second (MFMAC/s) -1 GFlop/s-with an energy efficiency up to 15 MMAC/s/mW and 9 MFMAC/s/mW. These building blocks are supported by aggressive on-chip power conversion and management, enabling energy-proportional heterogeneous computing for always-on IoT end nodes improving performance by several orders of magnitude with respect to traditional single-core MCUs within a power envelope of 153 mW. We demonstrated the capabilities of the proposed SoC on a wide set of near-sensor processing kernels showing that Mr. Wolf can deliver performance up to 16.4 GOp/s with energy efficiency up to 274 MOp/s/mW on real-life applications, paving the way for always-on data analytics on high-bandwidth sensors at the edge of the Internet of Things. Mehr anzeigen
Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
IEEE Journal of Solid-State CircuitsBand
Seiten / Artikelnummer
Verlag
IEEEThema
Digital signal processors; dynamic voltage scaling; memory architecture; multicore processing; parallel architecturesOrganisationseinheit
03996 - Benini, Luca / Benini, Luca
Förderung
162524 - MicroLearn: Micropower Deep Learning (SNF)
732631 - Open Transprecision Computing (EC)