Capacitor-based architecture for AI hardware accelerators
Tuesday, July 10, 2018 - 09:31
in Mathematics & Economics
IBM is reaching beyond digital technologies with a capacitor-based cross-point array for analog neural networks, exhibiting potential orders of magnitude improvements in deep learning computations. Analog computing architectures exploit the storage capability and physical attributes of certain memory devices not just to store information, but also to perform computations. This has the potential to greatly reduce the time and energy required by computers because data doesn't need to be shuttled between the memory and processor. The drawback could be a reduction in computational accuracy, but for systems that do not require high accuracy, it is the right trade-off.