The viability of analog-based accelerators for neuromorphic computing: a survey
The viability of analog-based accelerators for neuromorphic computing: a survey
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DOI：
10.1088/2634-4386/ac0242
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发表时间：
2021-05
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影响因子：
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通讯作者：
Mirembe Musisi-Nkambwe;Sahra Afshari;H. Barnaby;M. Kozicki;Ivan Sanchez Esqueda
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文献类型：
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作者：
Mirembe Musisi-Nkambwe;Sahra Afshari;H. Barnaby;M. Kozicki;Ivan Sanchez Esqueda
深度神经网络硬件研究的重点是减少记忆提取的延迟，这已经引导了基于模拟的人工神经网络（ANN）的方向。使用基于内存中计算/内存中处理的交叉非易失性内存（NVM）技术，可以降低延迟、提高计算并行性和提高存储密度，但这并非没有缺点。本文综述了这一丰富的领域，并强调了新兴的NVMs作为各种神经网络类型和应用中的多级突触仿真器的优势和挑战。讨论了在横杆矩阵中可靠地编程这些器件的当前和潜在方法，以及可靠地集成和传播矩阵乘积的技术，以模拟整个神经网络中众所周知的类似mac的操作。本文补充了之前的调查，但最重要的是揭示了在硬件加速器背景下基于最先进的NVM技术的基于模拟的人工神经网络实现可行性的进一步研究领域。虽然以前许多基于模拟的人工神经网络的综述都集中在设备特性上，但本文提出了交叉棒阵列、外围电路以及新兴内存交叉棒神经网络所需的架构和系统考虑因素的观点。
Focus in deep neural network hardware research for reducing latencies of memory fetches has steered in the direction of analog-based artificial neural networks (ANN). The promise of decreased latencies, increased computational parallelism, and higher storage densities with crossbar non-volatile memory (NVM) based in-memory-computing/processing-in-memory techniques is not without its caveats. This paper surveys this rich landscape and highlights the advantages and challenges of emerging NVMs as multi-level synaptic emulators in various neural network types and applications. Current and potential methods for reliably programming these devices in a crossbar matrix are discussed, as well as techniques for reliably integrating and propagating matrix products to emulate the well-known MAC-like operations throughout the neural network. This paper complements previous surveys, but most importantly uncovers further areas of ongoing research relating to the viability of analog-based ANN implementations based on state-of-the-art NVM technologies in the context of hardware accelerators. While many previous reviews of analog-based ANN focus on device characteristics, this review presents the perspective of crossbar arrays, peripheral circuitry and the required architectural and system considerations for an emerging memory crossbar neural network.