37 citations · 42 across the 6 of their papers we have counts for
6 papers
ZK-Flex: A Flexible and Scalable Framework for Accelerating Zero-Knowledge Proofs
Adiwena Putra, Cuong Manh Duong, Anh Quang Pham +1
Zero-knowledge proofs (ZKP) allows a prover to convince a verifier of computational correctness without revealing private data, ensuring both privacy and verifiability. However, pr…
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning
Je Yang, JaeUk Kim, Joo-Young Kim
Multi-agent reinforcement learning (MARL) is a powerful technology to construct interactive artificial intelligent systems in various applications such as multi-robot control and s…
Accelerating Large-Scale Graph-based Nearest Neighbor Search on a Computational Storage Platform
Ji-Hoon Kim, Yeo-Reum Park, Jaeyoung Do +2
K-nearest neighbor search is one of the fundamental tasks in various applications and the hierarchical navigable small world (HNSW) has recently drawn attention in large-scale clou…
FSHMEM: Supporting Partitioned Global Address Space on FPGAs for Large-Scale Hardware Acceleration Infrastructure
Yashael Faith Arthanto, David Ojika, Joo-Young Kim
By providing highly efficient one-sided communication with globally shared memory space, Partitioned Global Address Space (PGAS) has become one of the most promising parallel compu…
Exploration of Systolic-Vector Architecture with Resource Scheduling for Dynamic ML Workloads
Jung-Hoon Kim, Sungyeob Yoo, Seungjae Moon +1
As artificial intelligence (AI) and machine learning (ML) technologies disrupt a wide range of industries, cloud datacenters face ever-increasing demand in inference workloads. How…
FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism
Je Yang, Seongmin Hong, Joo-Young Kim
In this paper, we present a deep reinforcement learning platform named FIXAR which employs fixed-point data types and arithmetic units for the first time using a SW/HW co-design ap…