Abstract
This article presents a deep learning-based classifier IC for keyword spotting (KWS) in 65-nm CMOS designed using an algorithm-hardware co-design approach. First, a recurrent attention model (RAM) algorithm for the KWS task (the KeyRAM algorithm) is proposed. The KeyRAM algorithm enables accuracy versus energy scalability via a confidence-based computation (CC) scheme, leading to a 2.5 reduction in computational complexity compared to state-of-the-art (SOTA) neural networks, and is well-suited for in-memory computing (IMC) since the bulk (89%) of its computations are 4-b matrix-vector multiplies. The KeyRAM IC comprises a multi-bit multi-bank IMC architecture with a digital co-processor. A sparsity-aware summation scheme is proposed to alleviate the challenge faced by IMCs when summing sparse activations. The digital co-processor employs diagonal major weight storage to compute without any stalls. This combination of the IMC and digital processors enables a balanced tradeoff between energy efficiency and high accuracy computation. The resultant KWS IC achieves SOTA decision latency of 39.9 s with a decision energy < 0.5 J /dec which translates to more than 24 savings in the energy-delay product (EDP) of decisions over existing KWS ICs.
Original language | English (US) |
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Article number | 9239367 |
Pages (from-to) | 2234-2244 |
Number of pages | 11 |
Journal | IEEE Journal of Solid-State Circuits |
Volume | 56 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2021 |
Keywords
- In-memory computing (IMC)
- keyword spotting (KWS)
- machine learning
- recurrent attention networks
ASJC Scopus subject areas
- Electrical and Electronic Engineering