Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.
Nat Commun. 2022 Mar 29;13(1):1670. doi: 10.1038/s41467-022-29320-6.
In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a system level learning algorithm such that the ensemble trajectory is guided towards the optimal solution. We show that the extrinsic energy required for state trajectory modulation can be matched to the dynamics of neural network learning which leads to a significant reduction in energy-dissipated for memory updates during ML training. Thus, the proposed synapse array could have significant implications in addressing the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.
在本文中，我们提出了一种自适应突触阵列，可用于提高机器学习 (ML) 系统的能效。该突触阵列由一组模拟存储元件组成，每个存储元件本身就是一个微尺度动力系统，以其时间状态轨迹存储信息。然后，通过系统级学习算法对状态轨迹进行调制，以使整体轨迹朝着最优解的方向发展。我们表明，用于状态轨迹调制的外在能量可以与神经网络学习的动力学相匹配，从而显著减少 ML 训练过程中存储更新的能量消耗。因此，所提出的突触阵列可能对解决人工智能 (AI) 系统中观察到的训练和推理阶段之间的能效不平衡问题具有重要意义。
