Benchmarking High Bandwidth Memory on FPGAs
Not on the map. Why?
Abstract (arXiv)
FPGAs are starting to be enhanced with High Bandwidth Memory (HBM) as a way to reduce the memory bandwidth bottleneck encountered in some applications and to give the FPGA more capacity to deal with application state. However, the performance characteristics of HBM are still not well specified, especially in the context of FPGAs. In this paper, we bridge the gap between nominal specifications and actual performance by benchmarkingHBM on a state-of-the-art FPGA, i.e., a Xilinx Alveo U280 featuring a two-stack HBM subsystem. To this end, we propose Shuhai, a benchmarking tool that allows us to demystify all the underlying details of HBM on an FPGA. FPGA-based benchmarking should also provide a more accurate picture of HBM than doing so on CPUs/GPUs, since CPUs/GPUs are noisier systems due to their complex control logic and cache hierarchy. Since the memory itself is complex, leveraging custom hardware logic to benchmark inside an FPGA provides more details as well as accurate and deterministic measurements. We observe that 1) HBM is able to provide up to 425GB/s memory bandwidth, and 2) how HBM is used has a significant impact on performance, which in turn demonstrates the importance of unveiling the performance characteristics of HBM so as to select the best approach. As a yardstick, we also applyShuhaito DDR4to show the differences between HBM and DDR4.Shuhai can be easily generalized to other FPGA boards or other generations of memory, e.g., HBM3, and DDR3. We will makeShuhaiopen-source, benefiting the community
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 May 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 1.
5 papers.
- 2024FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAscited by 64
- 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Modelscited by 23
- 2022A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipeliningcited by 22
- 2024Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Smallcited by 4
- 2025PLASMA: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPUcited by 0
- 2025PLASMA: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPU21 Aug 2025
- 2024Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small21 Oct 2024
- 2024FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs8 Jan 2024
- 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Models15 Oct 2023
- 2022A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining7 Aug 2022