2025/12/15 by Endri Taka, Taka, Endri, Andre Roesti +9 · 1 voice
Computer Science · Engineering · #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Hardware Architecture (cs.AR) #Numerical Methods and Algorithms #Parallel Computing and Optimization Techniques #cs.AR
paper · pdf · doi:10.48550/arxiv.2512.13282
openalex publication_date 2025/12/15 · arxiv published 2025/12/15 · arxiv updated 2025/12/15 · openalex created_date 2025/12/17 · openalex updated_date 2026/07/28
The high computational and memory demands of modern deep learning (DL) workloads have led to the development of specialized hardware devices from cloud to edge, such as AMD's Ryzen AI XDNA NPUs. Optimizing general matrix multiplication (GEMM) algorithms for these architectures is critical for improving DL workload performance. To this end, this paper presents a common systematic methodology to optimize GEMM workloads across the two current NPU generations, namely XDNA and XDNA2. Our implementations exploit the unique architectural features of AMD's NPUs and address key performance bottlenecks at the system level. End-to-end performance evaluation across various GEMM sizes demonstrates state-of-the-art throughput of up to 6.76 TOPS (XDNA) and 38.05 TOPS (XDNA2) for 8-bit integer (int8) precision. Similarly, for brain floating-point (bf16) precision, our GEMM implementations attain up to 3.14 TOPS (XDNA) and 14.71 TOPS (XDNA2). This work provides significant insights into key performance aspects of optimizing GEMM workloads on Ryzen AI NPUs.