Supporting Energy-Based Learning with an Ising Machine Substrate: A Case Study on RBM
Supporting Energy-Based Learning with an Ising Machine Substrate: A Case Study on RBM
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DOI：
10.1145/3613424.3614315
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Uday Kumar Reddy Vengalam;Yongchao Liu;Tong Geng;Hui Wu;Michael Huang
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文献类型：
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作者：
Uday Kumar Reddy Vengalam;Yongchao Liu;Tong Geng;Hui Wu;Michael Huang
大自然显然会不断地进行大量计算。如果我们可以在适当的级别上利用一些计算，那么您可以比使用von Neumann计算机更快，更有效地执行某些类型的计算（更加有效）。实际上，许多强大的算法受自然的启发，因此是基于自然计算的主要候选者。这项工作的一个特殊分支已经看到了最近的一些快速进步。一些Ising机器已经显示出更好的性能和能源效率，以实现优化问题。通过设计迭代和硬件和算法之间的共同发展，我们希望将来从基于自然的计算系统中获得更多好处。在本文中，我们为使用基于能量的机器学习算法的增强式ISING机器提供了一个适合培训和推理的案例。我们表明，随着变化很小，Ising底物加速了算法的关键部分，并实现了非平凡的加速和效率增长。通过更实质性的变化，我们可以将机器变成自给自足的梯度追随者，以完全完成硬件的培训。与张量处理单元（TPU）主机相比，这可以带来约29倍的加速和能量减少约1000倍。
Nature apparently does a lot of computation constantly. If we can harness some of that computation at an appropriate level, we can potentially perform certain type of computation (much) faster and more efficiently than we can do with a von Neumann computer. Indeed, many powerful algorithms are inspired by nature and are thus prime candidates for nature-based computation. One particular branch of this effort that has seen some recent rapid advances is Ising machines. Some Ising machines are already showing better performance and energy efficiency for optimization problems. Through design iterations and co-evolution between hardware and algorithm, we expect more benefits from nature-based computing systems in the future. In this paper, we make a case for an augmented Ising machine suitable for both training and inference using an energy-based machine learning algorithm. We show that with a small change, the Ising substrate accelerates key parts of the algorithm and achieves non-trivial speedup and efficiency gain. With a more substantial change, we can turn the machine into a self-sufficient gradient follower to virtually complete training entirely in hardware. This can bring about 29x speedup and about 1000x reduction in energy compared to a Tensor Processing Unit (TPU) host.