Benchmarking Delay and Energy of Neural Inference Circuits
Neural network circuits and architectures are currently under active research for applications to artificial intelligence and machine learning. Their physical performance metrics (area, time, and energy) are estimated. Various types of neural networks (artificial, cellular, spiking, and oscillator)...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Journal on Exploratory Solid-State Computational Devices and Circuits |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8915808/ |
| Summary: | Neural network circuits and architectures are currently under active research for applications to artificial intelligence and machine learning. Their physical performance metrics (area, time, and energy) are estimated. Various types of neural networks (artificial, cellular, spiking, and oscillator) are implemented with multiple CMOS and beyond-CMOS (spintronic, ferroelectric, and resistive memory) devices. A consistent and transparent methodology is proposed and used to benchmark this comprehensive set of options across several application cases. Promising architecture/device combinations are identified. |
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| ISSN: | 2329-9231 |