Abstract
There is a significant need to build efficient non-von Neumann computing systems for highly data-centric artificial intelligence related applications. Brain-inspired computing is one such approach that shows significant promise. Memory is expected to play a key role in this form of computing and, in particular, phase-change memory (PCM), arguably the most advanced emerging non-volatile memory technology. Given a lack of comprehensive understanding of the working principles of the brain, brain-inspired computing is likely to be realized in multiple levels of inspiration. In the first level of inspiration, the idea would be to build computing units where memory and processing co-exist in some form. Computational memory is an example where the physical attributes and the state dynamics of memory devices are exploited to perform certain computational tasks in the memory itself with very high areal and energy efficiency. In a second level of brain-inspired computing using PCM devices, one could design a co-processor comprising multiple cross-bar arrays of PCM devices to accelerate the training of deep neural networks. PCM technology could also play a key role in the space of specialized computing substrates for spiking neural networks, and this can be viewed as the third level of brain-inspired computing using these devices.
Highlights
We are on the cusp of a revolution in artificial intelligence (AI) and cognitive computing
In a second level of brain-inspired computing using phase-change memory (PCM) devices, one could design a co-processor comprising multiple cross-bar arrays of PCM devices to accelerate the training of deep neural networks
PCM technology could play a key role in the space of specialized computing substrates for spiking neural networks, and this can be viewed as the third level of brain-inspired computing using these devices
Summary
We are on the cusp of a revolution in artificial intelligence (AI) and cognitive computing. When a current pulse (typically referred to as the SET pulse) is applied to a PCM device in the RESET state, such that the temperature reached in the cell via Joule heating is high, but below the melting temperature, a part of the amorphous region crystallizes. The first key property of PCM that enables braininspired computing is its ability to achieve not just two levels but a continuum of resistance or conductance values.3 This is typically achieved by creating intermediate phase configurations by the application of suitable partial RESET pulses.. The second key property that enables brain-inspired computing is the accumulative behavior arising from the crystallization dynamics. one can induce progressive reduction in the size of the amorphous region (and the device resistance) by the successive application of SET pulses with the same amplitude. II–IV, we will describe how the multi-level storage capability and the accumulative behavior can be exploited for brain-inspired computing
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