arXiv:2606. 06515v1 Announce Type: cross Abstract: Transformer-based networks have emerged as prominent AI models with state-of-the-art performance, which potentially pave the way toward artificial general intelligence (AGI).
By Rachmad Vidya Wicaksana Putra, Solomon Micheal Serunjogi, Mahmoud Rasras, Muhammad Shafique
ShatterQuant is a hardware-software co-designed framework that enables mixed-precision quantization within individual tensors by assigning different bit-widths to blocks of a weight projection. It couples precision granularity with processing element configuration, allowing each precision to determine an effective block height. The framework includes a hardware-aware post-training method based on block-level standard deviation and weight sensitivity, a ShatterQuant Transformer Accelerator supporting 1/2/4/8-bit weight precision, precision-dependent PE configuration, block rescaling, and integrated softmax and piecewise-linear nonlinearities, and an evaluation showing 1.5 TOPS, 760 GOPS/$mm^2$ area efficiency, and 2.8 TOPS/W energy efficiency on a TSMC 16nm PDK implementation.
By Mikolaj Walczak, Edward Humes, Chao Fang, Marian Verhelst, Tinoosh Mohsenin
arXiv:2605. 26092v4 Announce Type: replace-cross Abstract: The deployment of Large Language Models (LLMs) and Vision Transformers (ViTs) on edge devices is significantly constrained by memory limitations and the critical timing bottlenecks introduced by dense Multiply-Accumulate (MAC) arrays.
By Maoyang Xiang, Tao Luo, Bo Wang
arXiv:2607. 03652v1 Announce Type: cross Abstract: Transformer blocks are prevalent in large language model (LLM) but present deployment challenges due to their challenging computational and memory demands.
By Victor Agostinelli, Nicolas Bohm Agostini, Antonino Tumeo
arXiv:2607. 19421v1 Announce Type: cross Abstract: Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multiplications on microring-resonator (MRR) banks with high throughput and energy efficiency.
By Xuming Chen, Deniz Najafi, Mehrdad Morsali, Chengwei Zhou, Zahra Ghanaatianjobzari, Mahdi Nikdast, Shaahin Angizi, Gourav Datta
VortexChat is an agentic framework that autonomously performs end-to-end inverse design of integrated photonic devices from natural language specifications. It combines a large language model decision agent with topology generation, gradient-based refinement, and full-wave electromagnetic simulation in a closed-loop architecture, enabling iterative decomposition of design objectives and minimal human intervention. The system successfully generated devices meeting the Vortex100 Benchmark metrics and fabricated a broadband terahertz perfect vortex beam multiplexer that matched simulation predictions.
By Faqian Chong, Yulun Wu, Shilong Li, Andrew Forbes, Hongsheng Chen, Song Han