Given the explosive growth in data-centric cognitive computing and the imminent end of CMOS scaling laws, it is becoming increasingly clear that we need to transition to non-von Neumann computing architectures. A first step in this direction could be in-memory computing whereby certain computational tasks are performed in place in a specialized memory unit, which we call computational memory.
Phase-change memory (PCM) devices could play a key role as elements of such a computational memory unit. The physical attributes of these devices can be exploited to achieve in-place computation. When organized in a cross-bar configuration, PCM devices can be used to perform matrix-vector multiplications with very low computational complexity . An appealing application of this concept is for the problem of compressed sensing and recovery of high-dimensional signals . This is an application in which the lack of precision arising from the matrix-vector multiplication operations is not prohibitive. However, in other applications such as solving systems of linear equations or training deep neural networks, the lack of precision could be a key challenge. To address this, we propose the concept of mixed-precision in-memory computing in which, through a judicious combination of high-precision processing units and computational memory, we can achieve arbitrarily high precision, while still retaining much of the benefits of non-von Neumann computing . Finally, I will present applications in which the dynamics of PCM devices, such as the crystallization dynamics and the dynamics of structural relaxation, are used to perform computational tasks. Examples include finding factors of numbers in parallel  and the detection of temporal correlations between event-based data streams .
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