Toward All-Digital Time-Domain Neural Network Accelerators for In-Sensor Processing Applications
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A4 Artikkeli konferenssijulkaisussa
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en
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6
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2023 IEEE Nordic Circuits and Systems Conference, NorCAS 2023 - Proceedings, pp. 1-6
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
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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