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Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
Proceedings of the IEEE ( IF 10.252 ) Pub Date : 2021-04-06 , DOI: 10.1109/jproc.2021.3067593
Mike Davies, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, Sumedh R. Risbud

Deep artificial neural networks apply principles of the brain’s information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by the form and function of biological neural circuits, so they can process new knowledge, adapt, behave, and learn in real time at low power levels. Despite several decades of research, until recently, very few published results have shown that today’s neuromorphic chips can demonstrate quantitative computational value. This is now changing with the advent of Intel’s Loihi, a neuromorphic research processor designed to support a broad range of spiking neural networks with sufficient scale, performance, and features to deliver competitive results compared to state-of-the-art contemporary computing architectures. This survey reviews results that are obtained to date with Loihi across the major algorithmic domains under study, including deep learning approaches and novel approaches that aim to more directly harness the key features of spike-based neuromorphic hardware. While conventional feedforward deep neural networks show modest if any benefit on Loihi, more brain-inspired networks using recurrence, precise spike-timing relationships, synaptic plasticity, stochasticity, and sparsity perform certain computation with orders of magnitude lower latency and energy compared to state-of-the-art conventional approaches. These compelling neuromorphic networks solve a diverse range of problems representative of brain-like computation, such as event-based data processing, adaptive control, constrained optimization, sparse feature regression, and graph search.

中文翻译:

用Loihi推进神经形态计算:结果和前景调查

深度人工神经网络应用了大脑信息处理的原理,从而导致了跨越许多问题领域的机器学习领域的突破。神经形态计算的目标是将这一步骤更进一步,以更直接地受生物神经回路的形式和功能启发的芯片,从而使它们能够以低功率水平实时处理新知识,适应,行为和学习。尽管进行了数十年的研究,但直到最近,很少有已发表的结果表明当今的神经形态芯片可以证明定量的计算价值。现在,随着英特尔Loihi(一种神经形态研究处理器)的出现,这种情况正在改变,该处理器旨在支持具有足够规模,性能,与最先进的当代计算架构相比,它具有可提供竞争性结果的功能。这项调查回顾了迄今为止Loihi在研究的主要算法领域中获得的结果,包括深度学习方法和旨在更直接地利用基于尖峰的神经形态硬件的关键特征的新颖方法。传统的前馈深层神经网络对Loihi的益处不大,但更多的大脑启发式网络使用递归,精确的尖峰定时关系,突触可塑性,随机性和稀疏性进行某些计算,与状态相比,延迟和能量降低了几个数量级。最先进的常规方法。这些引人注目的神经形态网络解决了代表类脑计算的各种问题,
更新日期:2021-05-04
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