33 citations · 50 across the 14 of their papers we have counts for
15 papers
AgileOS: A GPU Operating System Layer for Protected CUDA Services
Zhuoping Yang, Yiyu Shi, Alex Jones +1
Modern GPU applications increasingly interact with storage systems, network devices, vendor libraries, and GPU-resident services rather than executing only isolated compute kernels…
μ-ORCA: Optimizing Acceleration for Microsecond-Scale Deep Neural Network Inference on ACAP
Shixin Ji, Jinming Zhuang, Zhuoping Yang +3
Heterogeneous reconfigurable platforms with tensor cores, such as AMD ACAP, are increasingly adopted for deep neural network (DNN) inference due to their high throughput and flexib…
DORA: Dataflow-Instruction Orchestration Architecture for DNN Acceleration
Xingzhen Chen, Zhuoping Yang, Jinming Zhuang +5
As deep neural networks develop significantly more diverse and complex, achieving high performance and efficiency on complicated DNN models faces pressing challenges. Modern DNN wo…
FILCO: Flexible Composing Architecture with Real-Time Reconfigurability for DNN Acceleration
Xingzhen Chen, Jinming Zhuang, Zhuoping Yang +5
With the development of deep neural network (DNN) enabled applications, achieving high hardware resource efficiency on diverse workloads is non-trivial in heterogeneous computing p…
PHAROS: Pipelined Heterogeneous Accelerators for Real-time Safety-critical Systems With Deadline Compliance
Shixin Ji, Jinming Zhuang, Sarah Schultz +6
Spatially partitioned heterogeneous accelerators (HAs) are increasingly adopted in embedded systems for their performance and flexibility. Yet most existing HA design frameworks op…
Report for NSF Workshop on Algorithm-Hardware Co-design for Medical Applications
Peipei Zhou, Zheng Dong, Insup Lee +8
This report summarizes the discussions and recommendations from the NSF Workshop on Algorithm-Hardware Co-design for Medical Applications, held on September 26-27, 2024, in Pittsbu…
AGILE: Lightweight and Efficient Asynchronous GPU-SSD Integration
Zhuoping Yang, Jinming Zhuang, Xingzhen Chen +2
GPUs are critical for compute-intensive applications, yet emerging workloads such as recommender systems, graph analytics, and data analytics often exceed GPU memory capacity. Exis…
Advancing Environmental Sustainability in Data Centers via Carbon Depreciation Models
Shixin Ji, Zhuoping Yang, Xingzhen Chen +2
Recent improvements in energy efficiency and renewable energy integration have increased the relative importance of embodied carbon in data centers, motivating improved provisionin…
SSR: Spatial Sequential Hybrid Architecture for Latency Throughput Tradeoff in Transformer Acceleration
Jinming Zhuang, Zhuoping Yang, Shixin Ji +5
With the increase in the computation intensity of the chip, the mismatch between computation layer shapes and the available computation resource significantly limits the utilizatio…
SCARIF: Towards Carbon Modeling of Cloud Servers with Accelerators
Shixin Ji, Zhuoping Yang, Xingzhen Chen +5
Embodied carbon has been widely reported as a significant component in the full system lifecycle of various computing systems' green house gas emissions. Many efforts have been und…
REFRESH FPGAs: Sustainable FPGA Chiplet Architectures
Peipei Zhou, Jinming Zhuang, Stephen Cahoon +6
There is a growing call for greater amounts of increasingly agile computational power for edge and cloud infrastructure to serve the computationally complex needs of ubiquitous com…
Challenges and Opportunities to Enable Large-Scale Computing via Heterogeneous Chiplets
Zhuoping Yang, Shixin Ji, Xingzhen Chen +4
Fast-evolving artificial intelligence (AI) algorithms such as large language models have been driving the ever-increasing computing demands in today's data centers. Heterogeneous c…
AIM: Accelerating Arbitrary-precision Integer Multiplication on Heterogeneous Reconfigurable Computing Platform Versal ACAP
Zhuoping Yang, Jinming Zhuang, Jiaqi Yin +3
Arbitrary-precision integer multiplication is the core kernel of many applications in simulation, cryptography, etc. Existing acceleration of arbitrary-precision integer multiplica…
AutoMM: Energy-Efficient Multi-Data-Type Matrix Multiply Design on Heterogeneous Programmable System-on-Chip
Jinming Zhuang, Zhuoping Yang, Peipei Zhou
As the increasing complexity of Neural Network(NN) models leads to high demands for computation, AMD introduces a heterogeneous programmable system-on-chip (SoC), i.e., Versal ACAP…
CHARM: Composing Heterogeneous Accelerators for Matrix Multiply on Versal ACAP Architecture
Jinming Zhuang, Jason Lau, Hanchen Ye +10
Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning applications. To cope with the high computation demands of these applications, heterogene…