Traditional processor architectures are failing to keep up with the exploding compute demands of AI workloads. They are limited by the power-hungry weight-fetch of von Neumann architectures and limitations of transistor and frequency scaling. At-memory computation places compute elements directly next to the memory array, providing reduced power consumption and increased throughput due to the massive parallelism and memory bandwidth provided by the architecture.