Deep Neural Networks (DNNs) computation-hungry algorithms demand hardware platforms capable of meeting rigid power and timing requirements. We introduce the Serial-MAC-engine (SMAC-engine), a fully-digital hardware accelerator for inference of quantized DNNs suitable for integration in a heterogeneous System-on-Chip (SoC). The accelerator is completely embedded in the form of a Hardware Processing Engine (HWPE) in the PULPissimo platform, a RISCV-based programmable architecture that targets the computational requirements of IoT applications. The SMAC-engine supports configurable precision for both weights (8/6/4 bits) and activations (8/4 bits), with scalable performance. Results in 65 nm technology demonstrate that the serial-MAC approach enables the accelerator to achieve a maximum throughput of 14.28 GMAC/s, consuming 0.58 pJ/MAC@1.0 V when operating at a precision of 4 bits for weights and 8 bits for activations.

A Multi-Precision Bit-Serial Hardware Accelerator IP for Deep Learning Enabled Internet-of-Things

Conti F.;
2021

Abstract

Deep Neural Networks (DNNs) computation-hungry algorithms demand hardware platforms capable of meeting rigid power and timing requirements. We introduce the Serial-MAC-engine (SMAC-engine), a fully-digital hardware accelerator for inference of quantized DNNs suitable for integration in a heterogeneous System-on-Chip (SoC). The accelerator is completely embedded in the form of a Hardware Processing Engine (HWPE) in the PULPissimo platform, a RISCV-based programmable architecture that targets the computational requirements of IoT applications. The SMAC-engine supports configurable precision for both weights (8/6/4 bits) and activations (8/4 bits), with scalable performance. Results in 65 nm technology demonstrate that the serial-MAC approach enables the accelerator to achieve a maximum throughput of 14.28 GMAC/s, consuming 0.58 pJ/MAC@1.0 V when operating at a precision of 4 bits for weights and 8 bits for activations.
Midwest Symposium on Circuits and Systems
192
197
Capra M.; Conti F.; Martina M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/847025
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