Training Physical Neural Networks for Analog In-Memory Computing

• URL: http://arxiv.org/abs/2412.09010v1
• Date: Thu, 12 Dec 2024 07:22:23 GMT
• Title: Training Physical Neural Networks for Analog In-Memory Computing
• Authors: Yusuke Sakemi, Yuji Okamoto, Takashi Morie, Sou Nobukawa, Takeo Hosomi, Kazuyuki Aihara,
• Abstract summary: This paper presents physical neural networks (PNNs) for constructing physical models of IMC. We show that hardware non-idealities traditionally viewed as detrimental can enhance the model's learning performance.
• Score: 5.582327246405357
• License:
• Abstract: In-memory computing (IMC) architectures mitigate the von Neumann bottleneck encountered in traditional deep learning accelerators. Its energy efficiency can realize deep learning-based edge applications. However, because IMC is implemented using analog circuits, inherent non-idealities in the hardware pose significant challenges. This paper presents physical neural networks (PNNs) for constructing physical models of IMC. PNNs can address the synaptic current's dependence on membrane potential, a challenge in charge-domain IMC systems. The proposed model is mathematically equivalent to spiking neural networks with reversal potentials. With a novel technique called differentiable spike-time discretization, the PNNs are efficiently trained. We show that hardware non-idealities traditionally viewed as detrimental can enhance the model's learning performance. This bottom-up methodology was validated by designing an IMC circuit with non-ideal characteristics using the sky130 process. When employing this bottom-up approach, the modeling error reduced by an order of magnitude compared to conventional top-down methods in post-layout simulations.

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