5 papers · 1 filter
From Buffers to Registers: Unlocking Fine-Grained FlashAttention with Hybrid-Bonded 3D NPU Co-Design
Jinxin Yu, Yudong Pan, Mengdi Wang +4
Transformer-based models dominate modern AI workloads but exacerbate memory bottlenecks due to their quadratic attention complexity and ever-growing model sizes. Existing accelerat…
LLMulator: Generalizable Cost Modeling for Dataflow Accelerators with Input-Adaptive Control Flow
Kaiyan Chang, Wenlong Zhu, Shengwen Liang +2
Accurate and fast performance prediction for dataflow-based accelerators is vital for efficient hardware design and design space exploration, yet existing methods struggle to gener…
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
Yintao He, Haiyu Mao, Christina Giannoula +6
Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate ou…
Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation
Kaiyan Chang, Zhirong Chen, Yunhao Zhou +9
Natural language interfaces have exhibited considerable potential in the automation of Verilog generation derived from high-level specifications through the utilization of large la…
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
Kaiyan Chang, Kun Wang, Nan Yang +16
Recent advances in large language models have demonstrated their potential for automated generation of hardware description language (HDL) code from high-level prompts. Researchers…