비동기 설계 방식기반의 저전력 뉴로모픽 하드웨어의 설계 및 구현
Low Power Neuromorphic Hardware Design and Implementation Based on Asynchronous Design Methodology
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This paper proposes an asynchronous circuit design methodology using a new Single Gate Sleep Convention Logic (SG-SCL) with advantages such as low area overhead, low power consumption compared with the conventional null convention logic (NCL) methodologies. The delay-insensitive NCL asynchronous circuits consist of dual-rail structures using {DATA0, DATA1, NULL} encoding which carry a significant area overhead by comparison with single-rail structures. The area overhead can lead to high power consumption. In this paper, the proposed single gate SCL deploys a power gating structure for a new {DATA, SLEEP} encoding to achieve low area overhead and low power consumption maintaining high performance during DATA cycle. In this paper, the proposed methodology has been evaluated by a liquid state machine (LSM) for pattern and digit recognition using FPGA and a 0.18 µm CMOS technology with a supply voltage of 1.8 V. the LSM is a neural network (NN) algorithm similar to a spiking neural network (SNN). The experimental results show that the proposed SG-SCL LSM reduced power consumption by 10% compared to the conventional LSM.
Abstract
This paper proposes an asynchronous circuit design methodology using a new Single Gate Sleep Convention Logic (SG-SCL) with advantages such as low area overhead, low power consumption compared with the conventional null convention logic (NCL) methodologies. The delay-insensitive NCL asynchronous circuits consist of dual-rail structures using {DATA0, DATA1, NULL} encoding which carry a significant area overhead by comparison with single-rail structures. The area overhead can lead to high power consumption. In this paper, the proposed single gate SCL deploys a power gating structure for a new {DATA, SLEEP} encoding to achieve low area overhead and low power consumption maintaining high performance during DATA cycle. In this paper, the proposed methodology has been evaluated by a liquid state machine (LSM) for pattern and digit recognition using FPGA and a 0.18 µm CMOS technology with a supply voltage of 1.8 V. the LSM is a neural network (NN) algorithm similar to a spiking neural network (SNN). The experimental results show that the proposed SG-SCL LSM reduced power consumption by 10% compared to the conventional LSM.
참고문헌 (6)
- H. Li, "Learning IoT in Edge : Deep Learning for the Internet of Things with Edge Computing", IEEE Netw, 32(1), pp.96~101 (2018)
- S. C. Smith, "Designing Asynchronous Circuits using NULL Convention Logic (NCL)" (2009), Morgan & Claypool Publishers
- F. A. Parsan, "Gate Mapping Automation for Asynchronous NULL Convention Logic Circuits", IEEE Trans. on VLSI Syst, 22(1), pp.99~112 (2014)
- Y. Liu, "Online adaptation and energy minimization for hardware recurrent spiking neural networks", ACM J. Emerg. Technol. Comput. Syst, 14(1), pp.11(1)~11(21) (2018)
- Q. Wang, "Energy efficient parallel neuromorphic architectures with approximate arithmetic on FPGA", Neurocomputing, 221(19), pp.58~146 (2017)
