Department of Electrical Engineering, Center for Integrated Systems, Stanford University Stanford, CA, USA ; School of Computing, Informatics, and Decision Systems Engineering, Arizona State University Tempe, AZ, USA.
Front Neurosci. 2013 Oct 31;7:186. doi: 10.3389/fnins.2013.00186. eCollection 2013.
Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.
硬件实现的神经形态计算作为一种超越传统数字计算的计算范例具有吸引力。在这项工作中，我们表明，在较弱的编程条件下，金属氧化物电阻式随机存储器的 SET（关-开）转变具有概率性。二进制突触器件的开关可变性实现了随机学习规则。对用于竞争学习的全加网络的模拟进行了这种随机 SET 转变的统计测量和建模。该模拟表明，通过这种随机学习，可以有效地实现输入模式的定向分类功能。在使用模拟突触的传统方法和使用利用随机学习的二进制突触的本工作方法之间比较了系统性能指标。在神经形态计算中使用二进制突触的可行性可能放宽了对工程连续多级中间状态的约束，并拓宽了突触器件设计的材料选择。
