16 citations · 27 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Sustainable AI Processing at the Edge
Sébastien Ollivier, Sheng Li, Yue Tang +5
Edge computing is a popular target for accelerating machine learning algorithms supporting mobile devices without requiring the communication latencies to handle them in the cloud.…
Best-Effort FPGA Programming: A Few Steps Can Go a Long Way
Jason Cong, Zhenman Fang, Yuchen Hao +4
FPGA-based heterogeneous architectures provide programmers with the ability to customize their hardware accelerators for flexible acceleration of many workloads. Nonetheless, such…
ARAPrototyper: Enabling Rapid Prototyping and Evaluation for Accelerator-Rich Architectures
Yu-Ting Chen, Jason Cong, Zhenman Fang +2
Compared to conventional general-purpose processors, accelerator-rich architectures (ARAs) can provide orders-of-magnitude performance and energy gains and are emerging as one of t…