Abstract
Spiking Neural Networks (SNNs) are closely related to the dynamics of the human brain and use spatiotemporal encoding of information to generate spikes. Implementing various neuronal models in hardware is a popular field of research aiming to mimic biological behavior. The leaky integrate-and-fire model of the neuron is generally chosen for hardware implementation owing to its simplicity and accuracy in modeling the neuron. This paper proposes an infinite impulse response (IIR) filter-based neuron model and describes a backpropagation-based training algorithm for an SNN built using the proposed neurons. The trained network is implemented on an Ultra96-V2 FPGA to validate the design and demonstrate the power and resource efficiency. The implemented design achieves an accuracy of 98.91% on the MNIST dataset and classifies images at 13,021 frames-per-second (FPS) with a 200 MHz clock while consuming < 700 mW of power. The proposed design achieves similar energy efficiency as previous works and approx 7.5× higher resource efficiency than previous publications.
Original language | English (US) |
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Title of host publication | ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665451093 |
DOIs | |
State | Published - 2023 |
Externally published | Yes |
Event | 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 - Monterey, United States Duration: May 21 2023 → May 25 2023 |
Publication series
Name | Proceedings - IEEE International Symposium on Circuits and Systems |
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Volume | 2023-May |
ISSN (Print) | 0271-4310 |
Conference
Conference | 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023 |
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Country/Territory | United States |
City | Monterey |
Period | 5/21/23 → 5/25/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- EMNIST
- F-MNIST
- IIR filter
- MNIST
- Spiking-neural network
- leaky integrate-and-fire model