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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2411.18271 |
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| _version_ | 1866916497413111808 |
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| author | Yang, Junyi Mao, Ruibin Jiang, Mingrui Cheng, Yichuan Sun, Pao-Sheng Vincent Dong, Shuai Pedretti, Giacomo Sheng, Xia Ignowski, Jim Li, Haoliang Li, Can Basu, Arindam |
| author_facet | Yang, Junyi Mao, Ruibin Jiang, Mingrui Cheng, Yichuan Sun, Pao-Sheng Vincent Dong, Shuai Pedretti, Giacomo Sheng, Xia Ignowski, Jim Li, Haoliang Li, Can Basu, Arindam |
| contents | Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models. In this work, we experimentally demonstrate the implementation of a non-linear activation function integrated with a ramp analog-to-digital conversion (ADC) at the periphery of the memory to improve in-memory implementation of RNNs. Our approach uses an extra column of memristors to produce an appropriately pre-distorted ramp voltage such that the comparator output directly approximates the desired nonlinear function. We experimentally demonstrate programming different nonlinear functions using a memristive array and simulate its incorporation in RNNs to solve keyword spotting and language modelling tasks. Compared to other approaches, we demonstrate manifold increase in area-efficiency, energy-efficiency and throughput due to the in-memory, programmable ramp generator that removes digital processing overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18271 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks Yang, Junyi Mao, Ruibin Jiang, Mingrui Cheng, Yichuan Sun, Pao-Sheng Vincent Dong, Shuai Pedretti, Giacomo Sheng, Xia Ignowski, Jim Li, Haoliang Li, Can Basu, Arindam Hardware Architecture Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models. In this work, we experimentally demonstrate the implementation of a non-linear activation function integrated with a ramp analog-to-digital conversion (ADC) at the periphery of the memory to improve in-memory implementation of RNNs. Our approach uses an extra column of memristors to produce an appropriately pre-distorted ramp voltage such that the comparator output directly approximates the desired nonlinear function. We experimentally demonstrate programming different nonlinear functions using a memristive array and simulate its incorporation in RNNs to solve keyword spotting and language modelling tasks. Compared to other approaches, we demonstrate manifold increase in area-efficiency, energy-efficiency and throughput due to the in-memory, programmable ramp generator that removes digital processing overhead. |
| title | Efficient Nonlinear Function Approximation in Analog Resistive Crossbars for Recurrent Neural Networks |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2411.18271 |