在人工智能如火如荼的今天,基于人脑的“脉冲”(spiking)模拟计算框架下的脉冲神经网络 (SNN)、神经形态计算(neuromorphic computing)有望在实现人工智能的同时,降低计算平台的能耗。这一跨学科领域以硅电路实现生物中的神经环路(circuit)为起点,现已发展到包括基于脉冲的编码以及事件驱动表示的算法的硬件实现。 2019 年...
在人工智能如火如荼的今天,基于人脑的“脉冲”(spiking)模拟计算框架下的脉冲神经网络 (SNN)、神经形态计算(neuromorphic computing)有望在实现人工智能的同时,降低计算平台的能耗。这一跨学科领域以硅电路实现生物中的神经环路(circuit)为起点,现已发展到包括基于脉冲的编码以及事件驱动表示的算法的硬件实现。 2019 年...
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3a). This fold is preceded by two additional short helices covering residues Thr523 to Asp554, serving as a buffer platform between the AC and the LH domain (Fig. 1a). Residues 590–610 constitute a flexible loop capping the LH catalytic site. Superposition with identified structural homologs...
566 2,084 175,046 17,707 508 3,489 5,289 69,945 10,547 64,814 66,166 6.209 6.208 6.203 6.201 6.199 6.194 6.193 6.192 6.188 6.183 6.181 6.170 6.167 6.164 6.162 6.162 6.155 6.155 6.154 6.145 6.142 6.140 6.139 6.138 6.137 6.134 6.133 6.131 6.129 6.129 6.125 6.120 6.111 6.102 6.100 6.093...
247, 523–547 (2016). 72. Duffie, D., Scheicher, M. & Vuillemey, G. Central clearing and collateral demand. J. Financial Econ.116, 237–256 (2015). 73. Heath, A., Kelly, G., Manning, M., Markose, S. & Shaghaghi, A. R. CCPs and network stability in OTC derivatives ...
247, 523–547 (2016). 72. Duffie, D., Scheicher, M. & Vuillemey, G. Central clearing and collateral demand. J. Financial Econ.116, 237–256 (2015). 73. Heath, A., Kelly, G., Manning, M., Markose, S. & Shaghaghi, A. R. CCPs and network stability in OTC derivatives ...
2019 年 11 月 28 日,普渡大学的 Kaushik Roy、 Akhilesh Jaiswal 和 Priyadarshini Panda 在 Nature 发表长文综述,概述了神经形态计算在算法和硬件方面的发展,介绍了学习和硬件框架的原理。以及神经形态计算的主要挑战以及发展前景,算法和硬件的协同设计等方面的内容。本文是全文翻译。