CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator

Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and performance compared to CPUs and GPUs. In this paper, we propose a novel cross-layer optimized neural network accelerator called CrossLight that leverages silicon photon...

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Veröffentlicht in:2021 58th ACM/IEEE Design Automation Conference (DAC) S. 1069 - 1074
Hauptverfasser: Sunny, Febin, Mirza, Asif, Nikdast, Mahdi, Pasricha, Sudeep
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 05.12.2021
Schlagworte:
Crosstalk
deep learning
Neural networks
Performance evaluation
Power lasers
Silicon photonics
Thermal engineering
Throughput
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Zusammenfassung:Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and performance compared to CPUs and GPUs. In this paper, we propose a novel cross-layer optimized neural network accelerator called CrossLight that leverages silicon photonics. CrossLight includes device-level engineering for resilience to process variations and thermal crosstalk, circuit-level tuning enhancements for inference latency reduction, and architecture-level optimizations to enable better resolution, energy-efficiency, and throughput. On average, CrossLight offers 9.5x lower energy-per-bit and 15.9x higher performance-per-watt than state-of-the-art photonic deep learning accelerators.
DOI:10.1109/DAC18074.2021.9586161