Memory and Information Processing in Neuromorphic Systems

By Giacomo Indiveri, Shih-Chii Liu. 2015

754 citations recorded by the source corpus.

A striking difference between brain-inspired neuromorphic processors and current von Neumann processor architectures is the way in which memory and processing is organized. As information and communication technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper, we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multineuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

A 0.086-mm$^2$ 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS (2018, neutral): “synapses) with massively-parallel data handling [6].”
A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs) (2017, methodology): “from the classical von Neumann architecture, as they are affected by the von Neumann bottleneck problem [14], [15].”
In-memory computing with resistive switching devices (2018, methodology): “This approach is similar to the computing scheme in the human brain, where information is processed in sparse networks of neurons and synapses, without any physical separation between computation and memory 16 .”
A survey of deep neural network architectures and their applications (2017, background): “Research on neuromorphic systems also supports the development of deep network model s [75].”
On-chip photonic synapse (2017, methodology): “INTRODUCTION In stark contrast to conventional computing systems based on the von Neumann architecture where the central computing unit is separated from the main memory, the human brain contains a large number of neurons with synapses, each of them acting as both the computing and the memory unit (”
Deep Learning With Spiking Neurons: Opportunities and Challenges (2018, background): “…powered robots and embedded devices, a particularly interesting application ﬁeld is brain-machine interfaces (Dethier et al., 2013; Corradi and Indiveri, 2015; Boi et al., 2016), where small size, low energy consumption, low heat dissipation, robustness, and the ability to decode in…”