CIM-BLAS: Computing-in-Memory Accelerator for BLAS

Basic Linear Algebra Subprograms (BLAS) is a foundational software library for linear algebra kernels, which is widely used in scientific and engineering computing. Existing BLAS accelerations mainly rely on CPUs and GPUs. Many operations in BLAS are data intensive, so they are constrained by the limited memory bandwidth of CPUs and GPUs. The computing-in-memory (CIM) technology can effectively alleviate the memory wall bottleneck and is particularly suitable for accelerating BLAS. In this paper, we propose the first CIM accelerator for BLAS, CIM-BLAS, based on non-volatile memory. CIM-BLAS includes a unified floating-point pipeline to support high-precision arithmetics. High efficiency of the accelerator is achieved by developing configurable data flows to support various BLAS functions. Compared with GPU implementations, CIMBLAS demonstrates several orders of magnitude performance and energy efficiency improvements for executing level-1 and level-2 BLAS functions, and can achieve an energy efficiency improvement of 2.6-24.1 $\times$ for executing level-3 BLAS functions. The improvement increases with the size of the matrix, indicating excellent scalability of CIM-BLAS. Application-level evaluations also demonstrate the potential of CIM for accelerating BLAS.