arXiv:2607. 26016v1 Announce Type: cross Abstract: Recently, photonic transformer accelerators (PTAs) have successfully achieved significant speedup and energy efficiency improvements over electronic accelerators for expediting Transformer inference.
By Solomon Micheal Serunjogi, Rachmad Vidya Wicaksana Putra, Ayat Taha, Muhammad Shafique, Mahmoud Rasras
PICasso is an AI‑enabled framework that converts natural‑language specifications into manufacturable silicon photonic integrated circuits (PICs) through a structured pipeline of NL → YAML → GDS, PDK‑aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX‑based photonic simulation. The authors introduce PIC‑Set, a benchmark of 36 parameterized PIC design tasks, and evaluate several large language models (LLMs) using new metrics such as structural and functional Spec@k, optimization efficiency, and robustness. Across the benchmark, PICasso markedly improves specification satisfaction, achieving up to 92.7% structural Spec@3 and 52% functional Spec@3, while reducing mean insertion loss from 4.98 dB to 3.25 dB through simulation‑guided optimization.
By Deepak Vungarala, Deniz Najafi, Abdulrahman Aljoudi, Zahra Ghanaatian, Navid Khoshavi, Gourav Datta, Arman Roohi, Mahdi Nikdast, Shaahin Angizi
arXiv:2606. 11117v1 Announce Type: cross Abstract: Designing FPGA-based accelerators for modern artificial intelligence workloads requires exploring a large and complex hardware design space that involves architectural parameters, data flow strategies, and memory hierarchies, making the process very time consuming.
By Vinamra Sharma, Xingjian Fu, Jude Haris, Jos\'e Cano
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:2608. 26418v1 Announce Type: cross Abstract: Modern AI workloads and the hardware that runs them evolve on different timescales: architectural definition precedes volume silicon by years, while target workloads shift in months.
By Architect Labs
arXiv:2607. 10942v1 Announce Type: cross Abstract: Physical AI systems, such as autonomous vehicles and intelligent machines, require transformer-based perception models that satisfy stringent edge latency and energy constraints.
By Ashiyana Abdul Majeed, Mahmoud Meribout, Neethu Joseph, Abel Kidane Haile, Mohammad Abdullah Al Faruque