Closed-Form Optimal Weight Splitting for Matrix-Vector Multiply on Noisy In-Memory Accelerators
Abstract: Analog in-memory computing performs the matrix-vector multiplications of neural networks directly inside the memory array, which saves time and energy. But each memory cell stores only a few levels and is noisy, so one cell cannot hold a weight precisely. The standard fix is to split each weight across several cells, like digits of a number. The base of this split decides the trade-off: a large base gives fine resolution but amplifies the noise, and a base near one averages the noise but wastes resolution. The best-known prior analysis considers only the noise, and so it always picks a base of one. We propose a principled method that also accounts for the limited number of levels per cell. It gives the optimal base in closed form, from a single measurement of the device noise. On a device-physics simulator, the optimal split is 3.3 dB more accurate than the common base-16 split, and it beats equal splitting in end-application accuracy.