arXiv:2512.00053v4 Announce Type: replace Abstract: Efficient mixed-precision MMA operations are critical for accelerating deep learning workloads on GPGPUs. However, existing open-source GPGPU Tensor Cores rely on discrete arithmetic units causing high latency, accumulated rounding errors, and poor resource utilization. To address these challenges, we propose Ten-Four, a configurable mixed-precision fused dot product (FEDP) unit microarchitecture implemented as part of the open-source RISC-V Vortex GPGPU's Tensor Core Unit extension. It supports low-precision multiplication in TF32, FP16, BF16, FP8, BF8, INT8 and INT4 with FP32/INT32 accumulation, native OCP Microscaling (MX), and sparse lane clock-gating for dynamic power reduction, while matching NVIDIA Tensor Core numerical accuracy.
Computer Science · 2026-11-10
Ten-Four: An Open-Source Fused Dot Product Unit for Mixed-Precision GPGPU Tensor Cores
arXiv:2512.00053v4 Announce Type: replace Abstract: Efficient mixed-precision MMA operations are critical for accelerating deep learning workloads on GPGPUs. However, existing open-source GPGPU Tensor Cores rely on discrete arithmetic units causing high latency, accumulated rounding errors, and poor resource utilization.
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