FINFET
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PALTO: Physics-Informed Active Learning for Tri-Gate FinFET Design Optimization for Vertical Power Delivery
Announce Type: new Abstract: This paper demonstrates the effectiveness of machine learning-driven optimization for designing application-specific GaN tri-gate FinFETs in vertical power delivery systems. Conventional TCAD-based approaches are computationally intensive and insufficient for navigating the high-dimensional, nonlinear design space of advanced GaN devices. To address this, a physics-informed active learning framework is used to intelligently guide simulations, accelerating...
O-POPE: High-Frequency Pipelined Outer Product based GEMM acceleration with minimal buffering overhead
arXiv:2606.02333v1 Announce Type: new Abstract: General matrix multiply (GEMM) dominates both execution time and energy consumption of modern machine learning (ML) workloads, placing increasing pressure on hardware efficiency. While quantization mitigates computational and data movement costs, accuracy-sensitive tasks such as training still require higher-precision floating-point formats. Existing floating-point GEMM accelerators face trade-offs between operating frequency, arithmetic...