---
_id: '63890'
abstract:
- lang: eng
text: The computation of highly contracted electron repulsion integrals (ERIs) is
essential to achieve quantum accuracy in atomistic simulations based on quantum
mechanics. Its growing computational demands make energy efficiency a critical
concern. Recent studies demonstrate FPGAs’ superior performance and energy efficiency
for computing primitive ERIs, but the computation of highly contracted ERIs introduces
significant algorithmic complexity and new design challenges for FPGA acceleration.In
this work, we present SORCERI, the first streaming overlay acceleration for highly
contracted ERI computations on FPGAs. SORCERI introduces a novel streaming Rys
computing unit to calculate roots and weights of Rys polynomials on-chip, and
a streaming contraction unit for the contraction of primitive ERIs. This shifts
the design bottleneck from limited CPU-FPGA communication bandwidth to available
FPGA computation resources. To address practical deployment challenges for a large
number of quartet classes, we design three streaming overlays, together with an
efficient memory transpose optimization, to cover the 21 most commonly used quartet
classes in realistic atomistic simulations. To address the new computation constraints,
we use flexible calculation stages with a free-running streaming architecture
to achieve high DSP utilization and good timing closure.Experiments demonstrate
that SORCERI achieves an average 5.96x, 1.99x, and 1.16x better performance per
watt than libint on a 64-core AMD EPYC 7713 CPU, libintx on an Nvidia A40 GPU,
and SERI, the prior best-performing FPGA design for primitive ERIs. Furthermore,
SORCERI reaches a peak throughput of 44.11 GERIS (109 ERIs per second) that is
1.52x, 1.13x, and 1.93x greater than libint, libintx and SERI, respectively. SORCERI
will be released soon at https://github.com/SFU-HiAccel/SORCERI.
author:
- first_name: Philip
full_name: Stachura, Philip
last_name: Stachura
- first_name: Xin
full_name: Wu, Xin
id: '77439'
last_name: Wu
- first_name: Christian
full_name: Plessl, Christian
id: '16153'
last_name: Plessl
orcid: 0000-0001-5728-9982
- first_name: Zhenman
full_name: Fang, Zhenman
last_name: Fang
citation:
ama: 'Stachura P, Wu X, Plessl C, Fang Z. SORCERI: Streaming Overlay Acceleration
for Highly Contracted Electron Repulsion Integral Computations in Quantum Chemistry.
In: Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable
Gate Arrays (FPGA ’26). Association for Computing Machinery; 2026:224-234.
doi:10.1145/3748173.3779198'
apa: 'Stachura, P., Wu, X., Plessl, C., & Fang, Z. (2026). SORCERI: Streaming
Overlay Acceleration for Highly Contracted Electron Repulsion Integral Computations
in Quantum Chemistry. Proceedings of the 2026 ACM/SIGDA International Symposium
on Field Programmable Gate Arrays (FPGA ’26), 224–234. https://doi.org/10.1145/3748173.3779198'
bibtex: '@inproceedings{Stachura_Wu_Plessl_Fang_2026, place={New York, NY, USA},
title={SORCERI: Streaming Overlay Acceleration for Highly Contracted Electron
Repulsion Integral Computations in Quantum Chemistry}, DOI={10.1145/3748173.3779198},
booktitle={Proceedings of the 2026 ACM/SIGDA International Symposium on Field
Programmable Gate Arrays (FPGA ’26)}, publisher={Association for Computing Machinery},
author={Stachura, Philip and Wu, Xin and Plessl, Christian and Fang, Zhenman},
year={2026}, pages={224–234} }'
chicago: 'Stachura, Philip, Xin Wu, Christian Plessl, and Zhenman Fang. “SORCERI:
Streaming Overlay Acceleration for Highly Contracted Electron Repulsion Integral
Computations in Quantum Chemistry.” In Proceedings of the 2026 ACM/SIGDA International
Symposium on Field Programmable Gate Arrays (FPGA ’26), 224–34. New York,
NY, USA: Association for Computing Machinery, 2026. https://doi.org/10.1145/3748173.3779198.'
ieee: 'P. Stachura, X. Wu, C. Plessl, and Z. Fang, “SORCERI: Streaming Overlay Acceleration
for Highly Contracted Electron Repulsion Integral Computations in Quantum Chemistry,”
in Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable
Gate Arrays (FPGA ’26), 2026, pp. 224–234, doi: 10.1145/3748173.3779198.'
mla: 'Stachura, Philip, et al. “SORCERI: Streaming Overlay Acceleration for Highly
Contracted Electron Repulsion Integral Computations in Quantum Chemistry.” Proceedings
of the 2026 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
(FPGA ’26), Association for Computing Machinery, 2026, pp. 224–34, doi:10.1145/3748173.3779198.'
short: 'P. Stachura, X. Wu, C. Plessl, Z. Fang, in: Proceedings of the 2026 ACM/SIGDA
International Symposium on Field Programmable Gate Arrays (FPGA ’26), Association
for Computing Machinery, New York, NY, USA, 2026, pp. 224–234.'
date_created: 2026-02-06T06:43:22Z
date_updated: 2026-02-09T09:16:32Z
department:
- _id: '27'
- _id: '518'
doi: 10.1145/3748173.3779198
keyword:
- electron repulsion integrals
- quantum chemistry
- atomistic simulation
- overlay architecture
- fpga acceleration
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/10.1145/3748173.3779198
page: 224-234
place: New York, NY, USA
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the 2026 ACM/SIGDA International Symposium on Field Programmable
Gate Arrays (FPGA '26)
publication_identifier:
isbn:
- '9798400720796'
publication_status: published
publisher: Association for Computing Machinery
status: public
title: 'SORCERI: Streaming Overlay Acceleration for Highly Contracted Electron Repulsion
Integral Computations in Quantum Chemistry'
type: conference
user_id: '77439'
year: '2026'
...
---
_id: '11950'
abstract:
- lang: eng
text: Advances in electromyographic (EMG) sensor technology and machine learning
algorithms have led to an increased research effort into high density EMG-based
pattern recognition methods for prosthesis control. With the goal set on an autonomous
multi-movement prosthesis capable of performing training and classification of
an amputee’s EMG signals, the focus of this paper lies in the acceleration of
the embedded signal processing chain. We present two Xilinx Zynq-based architectures
for accelerating two inherently different high density EMG-based control algorithms.
The first hardware accelerated design achieves speed-ups of up to 4.8 over the
software-only solution, allowing for a processing delay lower than the sample
period of 1 ms. The second system achieved a speed-up of 5.5 over the software-only
version and operates at a still satisfactory low processing delay of up to 15
ms while providing a higher reliability and robustness against electrode shift
and noisy channels.
author:
- first_name: Alexander
full_name: Boschmann, Alexander
last_name: Boschmann
- first_name: Andreas
full_name: Agne, Andreas
last_name: Agne
- first_name: Georg
full_name: Thombansen, Georg
last_name: Thombansen
- first_name: Linus Matthias
full_name: Witschen, Linus Matthias
id: '49051'
last_name: Witschen
- first_name: Florian
full_name: Kraus, Florian
last_name: Kraus
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
citation:
ama: Boschmann A, Agne A, Thombansen G, Witschen LM, Kraus F, Platzner M. Zynq-based
acceleration of robust high density myoelectric signal processing. Journal
of Parallel and Distributed Computing. 2019;123:77-89. doi:10.1016/j.jpdc.2018.07.004
apa: Boschmann, A., Agne, A., Thombansen, G., Witschen, L. M., Kraus, F., &
Platzner, M. (2019). Zynq-based acceleration of robust high density myoelectric
signal processing. Journal of Parallel and Distributed Computing, 123,
77–89. https://doi.org/10.1016/j.jpdc.2018.07.004
bibtex: '@article{Boschmann_Agne_Thombansen_Witschen_Kraus_Platzner_2019, title={Zynq-based
acceleration of robust high density myoelectric signal processing}, volume={123},
DOI={10.1016/j.jpdc.2018.07.004},
journal={Journal of Parallel and Distributed Computing}, publisher={Elsevier},
author={Boschmann, Alexander and Agne, Andreas and Thombansen, Georg and Witschen,
Linus Matthias and Kraus, Florian and Platzner, Marco}, year={2019}, pages={77–89}
}'
chicago: 'Boschmann, Alexander, Andreas Agne, Georg Thombansen, Linus Matthias Witschen,
Florian Kraus, and Marco Platzner. “Zynq-Based Acceleration of Robust High Density
Myoelectric Signal Processing.” Journal of Parallel and Distributed Computing
123 (2019): 77–89. https://doi.org/10.1016/j.jpdc.2018.07.004.'
ieee: A. Boschmann, A. Agne, G. Thombansen, L. M. Witschen, F. Kraus, and M. Platzner,
“Zynq-based acceleration of robust high density myoelectric signal processing,”
Journal of Parallel and Distributed Computing, vol. 123, pp. 77–89, 2019.
mla: Boschmann, Alexander, et al. “Zynq-Based Acceleration of Robust High Density
Myoelectric Signal Processing.” Journal of Parallel and Distributed Computing,
vol. 123, Elsevier, 2019, pp. 77–89, doi:10.1016/j.jpdc.2018.07.004.
short: A. Boschmann, A. Agne, G. Thombansen, L.M. Witschen, F. Kraus, M. Platzner,
Journal of Parallel and Distributed Computing 123 (2019) 77–89.
date_created: 2019-07-12T13:13:55Z
date_updated: 2022-01-06T06:51:13Z
department:
- _id: '78'
doi: 10.1016/j.jpdc.2018.07.004
intvolume: ' 123'
keyword:
- High density electromyography
- FPGA acceleration
- Medical signal processing
- Pattern recognition
- Prosthetics
language:
- iso: eng
page: 77-89
publication: Journal of Parallel and Distributed Computing
publication_identifier:
issn:
- 0743-7315
publication_status: published
publisher: Elsevier
status: public
title: Zynq-based acceleration of robust high density myoelectric signal processing
type: journal_article
user_id: '398'
volume: 123
year: '2019'
...