Toward All-Digital Time-Domain Neural Network Accelerators for In-Sensor Processing Applications
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Date
2023-11-01
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Language
en
Pages
6
1-6
1-6
Series
2023 IEEE Nordic Circuits and Systems Conference (NorCAS)
Abstract
Deep Neural Network (DNN) accelerators are increasingly integrated into sensing applications, such as wearables and sensor networks, to provide advanced in-sensor processing capabilities. Given wearables’ strict size and power requirements, minimizing the area and energy consumption of DNN accelerators is a critical concern. In that regard, computing DNN models in the time domain is a promising architecture, taking advantage of both technology scaling friendliness and efficiency. Yet, time-domain accelerators are typically not fully digital, limiting the full benefits of time-domain computation. In this work, we propose a time-domain multiply and accumulate (MAC) circuitry enabling an all-digital with a small size and low energy consumption to target in-sensor processing. The proposed MAC circuitry features a simple and efficient architecture without dependencies on analog non-idealities such as leakage and charge errors. It is implemented in 22nm FD-SOI technology, occupying 35 μm×35 μm while supporting multi-bit inputs (8-bit) and weights (4-bit). The power dissipation is 46.61 μW at 500MHz, and 20.58 μW at 200MHz. Combining 32 MAC units achieves an average power efficiency, area efficiency and normalized efficiency of 0.45 TOPS/W and 75 GOPS/mm2, and 14.4 1b-TOPS/W.Description
Keywords
Edge computing, Human activity recognition, Inertial measurement unit, In-sensor processing, Multiply-and-accumulate, Neural network accelerator, Smart sensor interface, Time-domain signal processing
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Citation
Mohey, A M, Kosunen, M, Ryynänen, J & Andraud, M 2023, Toward All-Digital Time-Domain Neural Network Accelerators for In-Sensor Processing Applications . in J Nurmi, P Ellervee, P Koch, F Moradi & M Shen (eds), 2023 IEEE Nordic Circuits and Systems Conference, NorCAS 2023 - Proceedings ., 10305470, IEEE, pp. 1-6, IEEE Nordic Circuits and Systems Conference, Aalborg, Denmark, 31/10/2023 . https://doi.org/10.1109/NorCAS58970.2023.10305470
