MPIM: Multi-purpose in-memory processing using configurable resistive memory

Mohsen Imani, Yeseong Kim, Tajana Rosing

Abstract: Running Internet of Things applications on general purpose processors results in a large energy and performance overhead, due to the high cost of data movement. Processing in-memory is a promising solution to reduce the data movement cost by processing the data locally inside the memory. In this paper, we design a Multi-Purpose In-Memory Processing (MPIM) system, which can be used as main memory and for processing. MPIM consists of multiple crossbar memories with the capability of efficient in-memory computations. Instead of transferring the large dataset to the processors, MPIM provides two important in-memory processing capabilities: i) data searching for the nearest neighbor ii) bitwise operations including OR, AND and XOR with small analog sense amplifiers. The experimental results show that the MPIM can achieve up to 5.5× energy savings and 19× speedup for the search operations as compared to AMD GPU-based implementation. For bitwise vector processing, we present 11000× energy improvements with 62× speedup over the SIMD-based computation, while outperforming other state-of-the-art in-memory processing techniques.