A TTFS-based energy and utilization efficient neuromorphic CNN accelerator
Spiking neural networks (SNNs), which are a form of neuromorphic, brain-inspired AI, have the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes that occur in SNN systems, also known as activations, tend to be extremely sparse, and low in number. This minimize...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1121592/full |
| _version_ | 1797833017042927616 |
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| author | Miao Yu Tingting Xiang Srivatsa P. Kyle Timothy Ng Chu Burin Amornpaisannon Yaswanth Tavva Venkata Pavan Kumar Miriyala Trevor E. Carlson |
| author_facet | Miao Yu Tingting Xiang Srivatsa P. Kyle Timothy Ng Chu Burin Amornpaisannon Yaswanth Tavva Venkata Pavan Kumar Miriyala Trevor E. Carlson |
| author_sort | Miao Yu |
| collection | DOAJ |
| description | Spiking neural networks (SNNs), which are a form of neuromorphic, brain-inspired AI, have the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes that occur in SNN systems, also known as activations, tend to be extremely sparse, and low in number. This minimizes the number of data accesses typically needed for processing. In addition, SNN systems are typically designed to use addition operations which consume much less energy than the typical multiply and accumulate operations used in DNN systems. The vast majority of neuromorphic hardware designs support rate-based SNNs, where the information is encoded by spike rates. Generally, rate-based SNNs can be inefficient as a large number of spikes will be transmitted and processed during inference. One coding scheme that has the potential to improve efficiency is the time-to-first-spike (TTFS) coding, where the information isn't presented through the frequency of spikes, but instead through the relative spike arrival time. In TTFS-based SNNs, each neuron can only spike once during the entire inference process, and this results in high sparsity. The activation sparsity of TTFS-based SNNs is higher than rate-based SNNs, but TTFS-based SNNs have yet to achieve the same accuracy as rate-based SNNs. In this work, we propose two key improvements for TTFS-based SNN systems: (1) a novel optimization algorithm to improve the accuracy of TTFS-based SNNs and (2) a novel hardware accelerator for TTFS-based SNNs that uses a scalable and low-power design. Our work in TTFS coding and training improves the accuracy of TTFS-based SNNs to achieve state-of-the-art results on the MNIST and Fashion-MNIST datasets. Meanwhile, our work reduces the power consumption by at least 2.4×, 25.9×, and 38.4× over the state-of-the-art neuromorphic hardware on MNIST, Fashion-MNIST, and CIFAR10, respectively. |
| first_indexed | 2024-04-09T14:18:05Z |
| format | Article |
| id | doaj.art-0dc3126568914d65bea0d0b16f4eda38 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-09T14:18:05Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-0dc3126568914d65bea0d0b16f4eda382023-05-05T05:34:56ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-05-011710.3389/fnins.2023.11215921121592A TTFS-based energy and utilization efficient neuromorphic CNN acceleratorMiao Yu0Tingting Xiang1Srivatsa P.2Kyle Timothy Ng Chu3Burin Amornpaisannon4Yaswanth Tavva5Venkata Pavan Kumar Miriyala6Trevor E. Carlson7School of Computing, Department of Computer Science, National University of Singapore, Singapore, SingaporeSchool of Computing, Department of Computer Science, National University of Singapore, Singapore, SingaporeSchool of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United StatesCentre for Quantum Technologies, National University of Singapore, Singapore, SingaporeSchool of Computing, Department of Computer Science, National University of Singapore, Singapore, SingaporeSchool of Computing, Department of Computer Science, National University of Singapore, Singapore, SingaporeSchool of Computing, Department of Computer Science, National University of Singapore, Singapore, SingaporeSchool of Computing, Department of Computer Science, National University of Singapore, Singapore, SingaporeSpiking neural networks (SNNs), which are a form of neuromorphic, brain-inspired AI, have the potential to be a power-efficient alternative to artificial neural networks (ANNs). Spikes that occur in SNN systems, also known as activations, tend to be extremely sparse, and low in number. This minimizes the number of data accesses typically needed for processing. In addition, SNN systems are typically designed to use addition operations which consume much less energy than the typical multiply and accumulate operations used in DNN systems. The vast majority of neuromorphic hardware designs support rate-based SNNs, where the information is encoded by spike rates. Generally, rate-based SNNs can be inefficient as a large number of spikes will be transmitted and processed during inference. One coding scheme that has the potential to improve efficiency is the time-to-first-spike (TTFS) coding, where the information isn't presented through the frequency of spikes, but instead through the relative spike arrival time. In TTFS-based SNNs, each neuron can only spike once during the entire inference process, and this results in high sparsity. The activation sparsity of TTFS-based SNNs is higher than rate-based SNNs, but TTFS-based SNNs have yet to achieve the same accuracy as rate-based SNNs. In this work, we propose two key improvements for TTFS-based SNN systems: (1) a novel optimization algorithm to improve the accuracy of TTFS-based SNNs and (2) a novel hardware accelerator for TTFS-based SNNs that uses a scalable and low-power design. Our work in TTFS coding and training improves the accuracy of TTFS-based SNNs to achieve state-of-the-art results on the MNIST and Fashion-MNIST datasets. Meanwhile, our work reduces the power consumption by at least 2.4×, 25.9×, and 38.4× over the state-of-the-art neuromorphic hardware on MNIST, Fashion-MNIST, and CIFAR10, respectively.https://www.frontiersin.org/articles/10.3389/fnins.2023.1121592/fullartificial neural networks (ANNs)brain-inspired networksneuromorphic hardwarespiking neural networks (SNNs)time-to-first-spike |
| spellingShingle | Miao Yu Tingting Xiang Srivatsa P. Kyle Timothy Ng Chu Burin Amornpaisannon Yaswanth Tavva Venkata Pavan Kumar Miriyala Trevor E. Carlson A TTFS-based energy and utilization efficient neuromorphic CNN accelerator Frontiers in Neuroscience artificial neural networks (ANNs) brain-inspired networks neuromorphic hardware spiking neural networks (SNNs) time-to-first-spike |
| title | A TTFS-based energy and utilization efficient neuromorphic CNN accelerator |
| title_full | A TTFS-based energy and utilization efficient neuromorphic CNN accelerator |
| title_fullStr | A TTFS-based energy and utilization efficient neuromorphic CNN accelerator |
| title_full_unstemmed | A TTFS-based energy and utilization efficient neuromorphic CNN accelerator |
| title_short | A TTFS-based energy and utilization efficient neuromorphic CNN accelerator |
| title_sort | ttfs based energy and utilization efficient neuromorphic cnn accelerator |
| topic | artificial neural networks (ANNs) brain-inspired networks neuromorphic hardware spiking neural networks (SNNs) time-to-first-spike |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1121592/full |
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