activity
20232026
most citedHMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing

2 citations · 2 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Arbor: Tree Search as a Cognition Layer for Autonomous Agents

Neha Prakriya, Chaojun Hou, Zheng Gong +5

Arbor is a multi-agent framework that introduces structured tree search as a cognition layer for autonomous agents operating in large, stateful action spaces. Prior autonomous opti…

cs.LG2025

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya, Zijian Ding, Yizhou Sun +1

FPGAs are increasingly adopted in datacenter environments for their reconfigurability and energy efficiency. High-Level Synthesis (HLS) tools have eased FPGA programming by raising…

cs.AI2024

Dynamic-Width Speculative Beam Decoding for Efficient LLM Inference

Zongyue Qin, Zifan He, Neha Prakriya +2

Large language models (LLMs) have shown outstanding performance across numerous real-world tasks. However, the autoregressive nature of these models makes the inference process slo…

cs.CL2024

Accelerating Large Language Model Pretraining via LFR Pedagogy: Learn, Focus, and Review

Neha Prakriya, Jui-Nan Yen, Cho-Jui Hsieh +1

Traditional Large Language Model (LLM) pretraining relies on autoregressive language modeling with randomly sampled data from web-scale datasets. Inspired by human learning techniq…

cs.CL2024

Optimized Multi-Token Joint Decoding with Auxiliary Model for LLM Inference

Zongyue Qin, Ziniu Hu, Zifan He +3

Large language models (LLMs) have achieved remarkable success across diverse tasks, yet their inference processes are hindered by substantial time and energy demands due to single-…

cs.CL2024★ 2 cited

HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing

Zifan He, Yingqi Cao, Zongyue Qin +3

Transformer-based large language models (LLM) have been widely used in language processing applications. However, due to the memory constraints of the devices, most of them restric…

cs.DC2023

TAPA-CS: Enabling Scalable Accelerator Design on Distributed HBM-FPGAs

Neha Prakriya, Yuze Chi, Suhail Basalama +2

Despite the increasing adoption of Field-Programmable Gate Arrays (FPGAs) in compute clouds, there remains a significant gap in programming tools and abstractions which can leverag…