FuriosaAI RNGD: A Tensor Contraction Processor for Sustainable AI Computing
Younggeun Choi, Junyoung Park, Sang Min Lee, Jeseung Yeon, Minho Kim, Chang-Jae Park, Byeongwook Bae, Hyunmin Jeong, Hanjoon Kim, June Paik, Nuno P. Lopes, Sungjoo Yoo · IEEE Micro · 2025
Modern artificial intelligence (AI) workloads require architectures capable of efficiently managing diverse tensor contraction patterns. Traditional approaches based on fixed-size matrix multiplications often fall short in scalability and flexibility. RNGD (pronounced “Renegade”), a second-generation tensor contraction processor, introduces an innovative architecture designed to exploit the parallelism and data locality inherent in tensor computations. Its coarse-grained processing elements (PEs) can operate as a unified large-scale unit or as multiple independent units, providing flexibility for various tensor shapes. Key innovations, such as a circuit switch-based fetch network, input broadcasting, and buffer-based reuse mechanisms, further enhance computational efficiency. RNGD represents a significant advancement in processor architecture, delivering optimized performance and energy efficiency for sustainable computation of next-generation AI workloads.