Heterogeneous Manycore In-Memory Computing Architectures

The growing use of deep learning has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. In-memory computing (IMC) architectures using non-volatile memory, such as resistive random-access memory (ReRAM), present a promising alternative. In addition to ReRAM, there are a plethora of IMC devices. Each device offers different advantages and drawbacks in terms of power, latency, area, and non-idealities. However, IMCs lack general-purpose computing capability. For instance, ReRAM crossbars are not suited for high-throughput division, which is needed for implementing normalization layers. In this paper, we present architectures that combine both (IMC and general-purpose computing) in an optimized manner to derive the best out of both worlds. The heterogeneous architectures combine the high-throughput multiplications of IMCs with the general-purpose computing ability of floating-point devices (such as CPU, GPU, etc.) to implement both training and inferencing of various AI algorithms.