FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning

• URL: http://arxiv.org/abs/2207.07958v1
• Date: Sat, 16 Jul 2022 14:30:15 GMT
• Title: FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning
• Authors: Javier Duarte and Nhan Tran and Ben Hawks and Christian Herwig and Jules Muhizi and Shvetank Prakash and Vijay Janapa Reddi
• Abstract summary: We present an initial set of scientific machine learning benchmarks, covering a variety of ML and embedded system techniques. These benchmarks can guide the design of future edge ML hardware for scientific applications capable of meeting the nanosecond and microsecond level latency requirements.
• Score: 6.281437279822099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is their need for ultra low-latency and on-detector ML capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled with the rapid advances in scientific instrumentation that is resulting in growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast ML at the edge is essential for reducing and filtering scientific data in real-time to accelerate science experimentation and enable more profound insights. To accelerate real-time scientific edge ML hardware and software solutions, we need well-constrained benchmark tasks with enough specifications to be generically applicable and accessible. These benchmarks can guide the design of future edge ML hardware for scientific applications capable of meeting the nanosecond and microsecond level latency requirements. To this end, we present an initial set of scientific ML benchmarks, covering a variety of ML and embedded system techniques.

Related papers

• Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey [51.87875066383221]
  This paper introduces fundamental concepts, traditional methods, and benchmark datasets, then examine the various roles Machine Learning plays in improving CFD. We highlight real-world applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, combustion, atmosphere & ocean science, biology fluid, plasma, symbolic regression, and reduced order modeling. We draw the conclusion that ML is poised to significantly transform CFD research by enhancing simulation accuracy, reducing computational time, and enabling more complex analyses of fluid dynamics.
  arXiv  Detail & Related papers  (2024-08-22T07:33:11Z)
• Reliable edge machine learning hardware for scientific applications [34.87898436984149]
  Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements.
  arXiv  Detail & Related papers  (2024-06-27T20:45:08Z)
• MESS: Modern Electronic Structure Simulations [0.0]
  Electronic structure simulation (ESS) has been used for decades to provide quantitative scientific insights on an atomistic scale. The recent introduction of machine learning (ML) into these domains has meant that ML models must be coded in languages such as FORTRAN and C. We introduce MESS: a modern electronic structure simulation package implemented in JAX; porting the ESS code to the ML world.
  arXiv  Detail & Related papers  (2024-06-05T10:15:16Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly [62.473245910234304]
  This paper takes a hardware-centric approach to explore how Large Language Models can be brought to modern edge computing systems. We provide a micro-level hardware benchmark, compare the model FLOP utilization to a state-of-the-art data center GPU, and study the network utilization in realistic conditions.
  arXiv  Detail & Related papers  (2023-10-04T20:27:20Z)
• TinyML: Tools, Applications, Challenges, and Future Research Directions [2.9398911304923456]
  TinyML is an embedded machine learning technique that enables ML applications on cheap, resource- and power-constrained devices. This article reviews various avenues available for TinyML implementation.
  arXiv  Detail & Related papers  (2023-03-23T15:29:48Z)
• Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics [77.34726150561087]
  Recent developments in artificial neural networks, particularly deep learning (DL), are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics.
  arXiv  Detail & Related papers  (2022-12-18T02:03:00Z)
• SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT [9.477629856092218]
  This paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML) SeLoC-ML enables non-experts to model, discover, reuse, and matchmake ML models and devices at scale. Developers can benefit from semantic application templates, called recipes, to fast prototype end-user applications.
  arXiv  Detail & Related papers  (2022-07-18T13:06:21Z)
• MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge [87.41163540910854]
  Deep neural network (DNN) latency characterization is a time-consuming process. We propose MAPLE-X which extends MAPLE by incorporating explicit prior knowledge of hardware devices and DNN architecture latency.
  arXiv  Detail & Related papers  (2022-05-25T11:08:20Z)
• Real-Time GPU-Accelerated Machine Learning Based Multiuser Detection for 5G and Beyond [70.81551587109833]
  nonlinear beamforming filters can significantly outperform linear approaches in stationary scenarios with massive connectivity. One of the main challenges comes from the real-time implementation of these algorithms. This paper explores the acceleration of APSM-based algorithms through massive parallelization.
  arXiv  Detail & Related papers  (2022-01-13T15:20:45Z)
• Applications and Techniques for Fast Machine Learning in Science [11.578814969632552]
  This report builds on two workshops held by the Fast ML for Science community. It covers three main areas: applications for fast ML across a number of scientific domains, techniques for training and implementing performant and resource-efficient algorithms, and computing architectures, platforms, and technologies for deploying these algorithms. This report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions.
  arXiv  Detail & Related papers  (2021-10-25T15:25:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.