The IBM Analog AI team is global team focusing on advancing the forefront of analog in-memory computing (AIMC) technologies to overcome the Von-Neumann Bottleneck. By performing vector-matrix multiplication (VMM) at the location where data is stored, we save time and energy by reducing data communication between memory and the compute unit. This efficient VMM computation is particularly powerful in deep neural network (DNN) applications, where the VMMs can tolerate a certain level of errors, for example, due to reduced-precision or noisy computations, without effecting accuracy of the DNN model. The team demonstrated near software-equivalent DNN accuracies both in phase change memory (PCM) hardware and in software simulations. We published high profile papers in Nature, VLSI, and IEDM.
The Silicon Valley Lab Research team holds technical leadership roles in a broad range of topics, including AIMC hardware testing, circuit design, algorithm simulations, application exploration, architecture definition, and software development. The team has hosted many intern students and visiting scholars in the past from around the world, including US, Europe, Brazil, Japan, and Taiwan. Some students are now regular employees at IBM and many continued to advance their careers in related fields.
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