![]() ![]() The collocation of processing and memory helps mitigate the von Neumann bottleneck regarding the processor/memory separation, which causes a slowdown in the maximum throughput that can be achieved. Although neurons are sometimes thought of as processing units and synapses are sometimes thought of as memory, the neurons and synapses both perform processing and store values in many implementations. ![]() Highly parallel operation: neuromorphic computers are inherently parallel, where all of the neurons and synapses can potentially be operating simultaneously however, the computations performed by neurons and synapses are relatively simple when compared with the parallelized von Neumann systems.Ĭollocated processing and memory: there is no notion of a separation of processing and memory in neuromorphic hardware. 1), neuromorphic computers present some fundamental operational differences: Given the aforementioned contrasting characteristics between the two architectures (Fig. Binary values can be turned into spikes and vice versa, but the precise way to perform this conversion is still an area of study in neuromorphic computing 3. In addition, while von Neumann computers encode information as numerical values represented by binary values, neuromorphic computers receive spikes as input, where the associated time at which they occur, their magnitude and their shape can be used to encode numerical information. Programs in neuromorphic computers are defined by the structure of the neural network and its parameters, rather than by explicit instructions as in a von Neumann computer. In a neuromorphic computer, on the other hand, both processing and memory are governed by the neurons and the synapses. Von Neumann computers are composed of separate CPUs and memory units, where data and instructions are stored in the latter. We define neuromorphic computers as non-von Neumann computers whose structure and function are inspired by brains and that are composed of neurons and synapses. ![]() The term neuromorphic was coined by Carver Mead in the late 1980s 1, 2, and at that time primarily referred to mixed analogue–digital implementations of brain-inspired computing however, as the field has continued to evolve and with the advent of large-scale funding opportunities for brain-inspired computing systems such as the DARPA Synapse project and the European Union’s Human Brain Project, the term neuromorphic has come to encompass a wider variety of hardware implementations. Neuromorphic computers are one such new computing technology. With the end of Moore’s law approaching and Dennard scaling ending, the computing community is increasingly looking at new technologies to enable continued performance improvements. ![]()
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