PFP: Parallel Floating-Point Vector Multiplication Acceleration in MAGIC ReRAM

Emerging applications, e.g., machine learning, large language models (LLMs), and graphic processing, are rapidly developing and are both compute-intensive and memory-intensive. Computing in Memory (CIM) is a promising architecture that accelerates these applications by eliminating the data movement between memory and processing units. Memristor-aided logic (MAGIC) CIM achieves massive parallelism, flexible computing, and non-volatility. However, MAGIC ReRAM performs floating-point (FP) vector multiplication sequentially, which wastes parallel computing resources and is limited by the array size. To solve this issue, we propose a parallel floating-point vector multiplication accelerator in MAGIC ReRAM. We exploit three levels of parallelism during the calculation of FP vector multiplication, referred to as PFP. First, we leverage the parallelism of MAGIC ReRAM. Second, we bring forward the final exponent to make the exponent calculations parallel. Third, we decouple the calculation of exponent, mantissa, and sign, which allows parallel calculation across accumulation. The experimental results show that PFP achieves a performance speedup of 2.51× and 15% energy savings compared to AritPIM when performing FP32 vector multiplication with a vector length of 512.