arXiv Machine Learning July 14, 2026 By Alexander Aoki, Gaetano Barone, Leena Diehl, Gabriele Giacomini, Vagelis Gkougkousis, Hanshal Goyal, Rohan Kher, Daniel Li, Anna Macchiolo, Yevhenii Padnuik, Daria Senina, Samantha Sunnarborg, Jessica Tang, Alessandro Tricoli, Lixing Wang, Don C. Wong

Machine Learning-Based Reconstruction for Resistive Silicon Sensors

Read the original on arXiv Machine Learning →

arXiv:2607. 11585v1 Announce Type: cross Abstract: Low-Gain Avalanche Diodes (LGADs) and AC-coupled Low-Gain Avalanche Diodes (AC-LGADs) are promising technologies for precision timing and four-dimensional tracking.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

llms

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Aug 5

NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory

arXiv:2608. 02700v1 Announce Type: cross Abstract: Analog compute-in-memory (CIM) enables energy-efficient neural network inference, but device variation and read noise can severely degrade low-bit quantized models.

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efficiency

arXiv AI

Jul 17

NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

arXiv:2607. 15123v1 Announce Type: cross Abstract: Recent FPGAs have improved deep learning (DL) inference efficiency through dedicated tensor blocks and in-BRAM computation.

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llms benchmarks

arXiv Machine Learning

Jun 9

AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction

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llms rag fine-tuning efficiency

arXiv AI

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HAMP-LIC: Hessian-Aware Mixed-Precision Post-Training Quantization for Learned Image Compression

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efficiency

arXiv AI

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llms fine-tuning efficiency benchmarks

arXiv AI

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MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation

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efficiency

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NANQ: Noise-Floor-Aware Mixed-Precision Non-Uniform Quantization for Analog Compute-in-Memory

NIFA: Nonlinear IMC enhanced FPGA for efficient ML inference

AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction

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MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation