Logic Synthesis for Digital In-Memory Computing

Processing in-memory is a promising solution strategy for accelerating data-intensive applications. While analog in-memory computing is extremely efficient, the limited precision is only acceptable for approximate computing applications. Digital in-memory computing provides the deterministic precision required to accelerate high assurance applications. State-of-the-art digital in-memory computing schemes rely on manually decomposing arithmetic operations into in-memory compute kernels. In contrast, traditional digital circuits are synthesized using complex and automated design flows. In this paper, we propose a logic synthesis framework called LOGIC for mapping high-level applications into digital in-memory compute kernels that can be executed using non-volatile memory. We first propose techniques to decompose element-wise arithmetic operations into in-memory kernels while minimizing the number of in-memory operations. Next, the sequence of the in-memory operation is optimized to minimize non-volatile memory utilization. Lastly, data layout re-organization is used to efficiently accelerate applications dominated by sparse matrix-vector multiplication operations. The experimental evaluations show that the proposed synthesis approach improves the area and latency of fixed-point multiplication by 77% and 20% over the state-of-the-art, respectively. On scientific computing applications from Suite Sparse Matrix Collection, the proposed design improves the area, latency and, energy by 3.6X, 2.6X, and 8.3X, respectively.