arXiv:2609.09254v1 Announce Type: cross Abstract: Field-programmable gate arrays (FPGAs) enable efficient neural-network inference, but most deployment flows either accelerate multiply-accumulate operations or convert pretrained quantized models into lookup tables (LUTs). We present DiffLUT-Net, an FPGA-native network connected by six-input LUTs that are trained from scratch. We jointly learn the 64 truth-table entries of a LUT and the source to each of its six input ports using a differentiable LUT function relaxation and hardware source selection. After training, the truth tables and connections are discretized, unused logic can be pruned, and the network is exported directly as synthesizable Verilog. Across five benchmarks, DiffLUT-Net achieves favorable accuracy-resource trade-offs. These results demonstrate the effectiveness of jointly learning LUT functions and sparse connectivity for compact FPGA-native inference. The code is available at https://github.com/TUDa-HWAI/DiffLUT-Network.

arXiv:2606.06510v4 Announce Type: replace-cross Abstract: We argue that on AI-optimised GPUs of the NVIDIA B300 generation and beyond, the FP8 tensor-core matrix operation, composed through CRT-based Ozaki Scheme II, can serve as the dominant matrix-work substrate for the surveyed matrix-dominated FP64 kernel classes at FP64-grade accuracy, with native FP64 recast from a hardware requirement into a derived accuracy guarantee. The claim is conditional: the FP8 op is the candidate dominant multiplication substrate, with a bounded auxiliary set of integer deconstruction/reconstruction work, FP32/Kulisch reductions, data movement and a native-FP64 fallback, organised as a hierarchy from the FP8 op through Ozaki II and the Berkeley dwarfs to applications. The instrument is the Tensor-Memory Equilibrium (TME) model, a Roofline extension with four parameters (compute multiplier $\alpha=3r+1$, bandwidth multiplier $\beta$, reconstruction cost $\gamma$, and the per-input deconstruction cost $c_q$ identified in an NVIDIA review) under which, at its upper bound, the reduction to FP8 costs no performance against an ideal native-FP64 machine of equal bandwidth. On-chip tile fusion drives $\beta \to 1$; the deconstruction term sets a threshold intensity below which emulation is conversion-bound. At the fused, engineered-$c_q$ bound every surveyed class reaches the memory roof, with two priced exceptions: large dense-square DGEMM sits at a deconstruction floor near 0.50 of the FP8 arithmetic roof (about 235 of 473 TFLOPS on the NVIDIA Rubin GPU), a liftable co-design coordinate, and the 3-D FFT is walled by a per-output integer epilogue at $4.9$-$6.7\times$ its roof in software, recoverable with minor hardware and one moderate ask. Ozaki II lifts the emulated FP64 ceiling from $\approx 1.3$ to $\approx 135$ TFLOPS on B300 and $\approx 473$ on Rubin; three deconstruction-path hardware options are given; constants are engine-checked.