A 22-nm All-Digital Time-Domain Neural Network Accelerator for Precision In-Sensor Processing
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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12
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IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Volume 32, issue 12, pp. 2220-2231
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 an all-digital time-domain accelerator with a small size and low energy consumption to target precision in-sensor processing like human activity recognition (HAR). The proposed accelerator features a simple and efficient architecture without dependencies on analog nonidealities such as leakage and charge errors. An eight-neuron layer (core computation layer) is implemented in 22-nm FD-SOI technology. The layer occupies 70 × 70 μ m while supporting multibit inputs (8-bit) and weights (8-bit) with signed accumulation up to 18 bits. The power dissipation of the computation layer is 576 μ W at 0.72-V supply and 500-MHz clock frequency achieving an average area efficiency of 24.74 GOPS/mm 2 (up to 544.22 GOPS/mm 2 ), an average energy efficiency of 0.21 TOPS/W (up to 4.63 TOPS/W), and a normalized energy efficiency of 13.46 1b-TOPS/W (up to 296.30 1b-TOPS/W).Description
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Mohey, A M, Leslin, J, Singh, G, Kosunen, M, Ryynänen, J & Andraud, M 2024, 'A 22-nm All-Digital Time-Domain Neural Network Accelerator for Precision In-Sensor Processing', IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 32, no. 12, 10758340, pp. 2220-2231. https://doi.org/10.1109/TVLSI.2024.3496090