FPnew: An Open-Source Multi-Format Floating-Point Unit Architecture for Energy-Proportional Transprecision Computing
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Abstract (arXiv)
The slowdown of Moore's law and the power wall necessitates a shift towards finely tunable precision (a.k.a. transprecision) computing to reduce energy footprint. Hence, we need circuits capable of performing floating-point operations on a wide range of precisions with high energy-proportionality. We present FPnew, a highly configurable open-source transprecision floating-point unit (TP-FPU) capable of supporting a wide range of standard and custom FP formats. To demonstrate the flexibility and efficiency of FPnew in general-purpose processor architectures, we extend the RISC-V ISA with operations on half-precision, bfloat16, and an 8bit FP format, as well as SIMD vectors and multi-format operations. Integrated into a 32-bit RISC-V core, our TP-FPU can speed up execution of mixed-precision applications by 1.67x w.r.t. an FP32 baseline, while maintaining end-to-end precision and reducing system energy by 37%. We also integrate FPnew into a 64-bit RISC-V core, supporting five FP formats on scalars or 2, 4, or 8-way SIMD vectors. For this core, we measured the silicon manufactured in Globalfoundries 22FDX technology across a wide voltage range from 0.45V to 1.2V. The unit achieves leading-edge measured energy efficiencies between 178 Gflop/sW (on FP64) and 2.95 Tflop/sW (on 8-bit mini-floats), and a performance between 3.2 Gflop/s and 25.3 Gflop/s.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 1; 2025: 1.
7 papers.
- 2021Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Modecited by 18
- 2022Minimum Variance Unbiased N:M Sparsity for the Neural Gradientscited by 9
- 2024Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Formatcited by 9
- 2023RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Accelerationcited by 4
- 2023PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applicationscited by 2
- 2023Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplicationcited by 1
- 2025Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computingcited by 0
- 2025Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing25 May 2025
- 2024Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format24 Nov 2024
- 2023Stella Nera: A Differentiable Maddness-Based Hardware Accelerator for Efficient Approximate Matrix Multiplication16 Nov 2023
- 2023PDPU: An Open-Source Posit Dot-Product Unit for Deep Learning Applications3 Feb 2023
- 2023RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration10 Jan 2023
- 2022Minimum Variance Unbiased N:M Sparsity for the Neural Gradients21 Mar 2022
- 2021Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode18 Oct 2021