arXiv AI June 8, 2026 By Rachmad Vidya Wicaksana Putra, Solomon Micheal Serunjogi, Mahmoud Rasras, Muhammad Shafique

DxPTA: An Architecture Design Space Exploration with Optical Dataflow-guided Strategy for HW/SW Co-Design of Photonic Transformer Accelerators

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).

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

arXiv AI

Jul 29

MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar

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

llms benchmarks

arXiv AI

Jun 10

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA

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

llms rag agents

arXiv AI

Jul 7

ELiTeFormer: An Efficient Transformer for FPGAs

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

llms efficiency benchmarks

arXiv AI

Jul 14

Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles

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

llms agents computer-vision efficiency

arXiv AI

Jun 26

A3C3: AI Algorithm and Accelerator Co-design, Co-search, and Co-generation

arXiv:2606. 20869v2 Announce Type: replace-cross Abstract: We present a holistic methodology for artificial intelligence algorithm and accelerator co-design, co-search, and co-generation (A3C3), which jointly optimizes neural network architectures and their hardware implementations to address the inefficiencies of traditional top-down AI system design flows.

By Selin Yildirim, Yingbing Huang, Deming Chen

efficiency

arXiv AI

Aug 10

HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation

arXiv:2608. 06791v1 Announce Type: cross Abstract: Application-specific FPGA accelerators offer substantial performance and energy-efficiency gains across many application domains, but developing them is costly, often requiring months of specialized effort.

By Yuebo Luo, Ahmad Sedigh Baroughi, Philip Stachura, Le Chen, Venkatram Vishwanath, Zhenman Fang, Caiwen Ding

llms agents fine-tuning benchmarks

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MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar

Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA

ELiTeFormer: An Efficient Transformer for FPGAs

Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles

A3C3: AI Algorithm and Accelerator Co-design, Co-search, and Co-generation

HLSmith: An Expert-Guided Agentic Framework for C/C++-to-HLS Translation