Abstract
As spatial and temporal resolutions of scientific instruments improve, the explosion in the volume of data produced is becoming a key challenge. It can be a critical bottleneck for integration between scientific instruments at the edge and high-performance computers/emerging accelerators. Placing data compression or reduction logic close to the data source is a possible approach to solve the bottleneck. However, the realization of such a solution requires the development of custom ASIC designs, which is still challenging in practice and tends to produce one-off implementations unusable beyond the initial intended scope. Therefore, as a feasibility study, we have been investigating a design workflow that allows us to explore algorithmically complex hardware designs and develop reusable hardware libraries for the needs of scientific instruments at the edge. Our vision is to cultivate our hardware development capability for streaming/dataflow hardware components that can be placed close to the data source to enable extreme data-intensive scientific experiments or environmental sensing. Furthermore, reducing data movement is essential to improving computing performance in general. Therefore, our co-design efforts on streaming hardware components can benefit computing applications other than scientific instruments. This vision paper discusses hardware specialization needs in scientific instruments and briefly reviews our progress leveraging the Chisel hardware description language and emerging open-source hardware ecosystems, including a few design examples.
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Notes
- 1.
The performance offered by a carefully optimized HLS implementation is comparable to that of an HDL implementation; however, resource usage is still in question.
- 2.
An open community for analog and mixed-signal IC and IP development and commercialization https://efabless.com/.
- 3.
Currently, only a select few technology nodes can be targeted with open-source EDA tools.
- 4.
FPGA deployments may require pipelining for the sake of achieving low latency.
- 5.
Significant lines of code, i.e., excluding comments.
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Acknowledgments
We thank Ian Foster and Kyle Chard for supporting this exciting collaboration between Argonne National Laboratory and the University of Chicago Department of Computer Science. We thank Pete Beckman and Alec Sandy for encouraging this multidisciplinary collaboration between the X-ray Science Division (XSD) and the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory. We also thank two anonymous referees for their useful comments. We thank Gail Pieper for editing this manuscript. The material is based upon work supported by Laboratory Directed Research and Development (LDRD 2021-0072) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under contract DE-AC02-06CH11357.
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Yoshii, K., Sankaran, R., Strempfer, S., Levental, M., Hammer, M., Miceli, A. (2022). A Hardware Co-design Workflow for Scientific Instruments at the Edge. In: Nichols, J., et al. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation. SMC 2021. Communications in Computer and Information Science, vol 1512. Springer, Cham. https://doi.org/10.1007/978-3-030-96498-6_12
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