Digital Neural Network Accelerators

Rueckert, Ulrich

doi:10.1007/978-3-030-18338-7_12

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Perhaps next year we will start seeing actual performance numbers that we can incorporate in this survey. There are many surveys [13]- [22] and other papers that cover various aspects of AI accelerators; this multi-year survey effort and this paper focus on gathering a comprehensive list of AI accelerators with their computational capability, power efficiency, and ultimately the computational effectiveness of utilizing accelerators in embedded and data center applications. Along with this focus, this paper mainly compares neural network accelerators that are useful for government and industrial sensor and data processing applications.…”

Section: Introductionmentioning

confidence: 99%

AI Accelerator Survey and Trends

Reuther,

Michaleas,

Jones

et al. 2021

Preprint

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Over the past several years, new machine learning accelerators were being announced and released every month for a variety of applications from speech recognition, video object detection, assisted driving, and many data center applications. This paper updates the survey of AI accelerators and processors from past two years. This paper collects and summarizes the current commercial accelerators that have been publicly announced with peak performance and power consumption numbers. The performance and power values are plotted on a scatter graph, and a number of dimensions and observations from the trends on this plot are again discussed and analyzed. This year, we also compile a list of benchmarking performance results and compute the computational efficiency with respect to peak performance.

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Section: Introductionmentioning

confidence: 99%

AI Accelerator Survey and Trends

Reuther,

Michaleas,

Jones

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Their goal is to use this technology to dramatically reduce SWaP for machine learning applications. There are many surveys [12], [21]- [29] and other papers that cover various aspects of AI accelerators; this paper focuses on gathering a comprehensive list of AI accelerators with their computational capability, power efficiency, and ultimately the computational effectiveness of utilizing accelerators in embedded and data center applications, as did last year's paper. Along with this focus, this paper mainly compares neural network accelerators that are useful for government and industrial sensor and data processing applications.…”

Section: Introductionmentioning

confidence: 99%

Survey and Benchmarking of Machine Learning Accelerators

Reuther

Michaleas

Jones

et al. 2019

2019 IEEE High Performance Extreme Computing Conference (HPEC)

153

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Advances in multicore processors and accelerators have opened the flood gates to greater exploration and application of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore's Law, have prompted an explosion of processors and accelerators that promise even greater computational and machine learning capabilities. These processors and accelerators are coming in many forms, from CPUs and GPUs to ASICs, FPGAs, and dataflow accelerators.This paper surveys the current state of these processors and accelerators that have been publicly announced with performance and power consumption numbers. The performance and power values are plotted on a scatter graph and a number of dimensions and observations from the trends on this plot are discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. We then select and benchmark two commerciallyavailable low size, weight, and power (SWaP) accelerators as these processors are the most interesting for embedded and mobile machine learning inference applications that are most applicable to the DoD and other SWaP constrained users. We determine how they actually perform with real-world images and neural network models, compare those results to the reported performance and power consumption values and evaluate them against an Intel CPU that is used in some embedded applications.On the left side of Figure 1, structured and unstructured data sources provide different views of entities and/or phenomenology. These raw data products are fed into a data conditioning step in which they are fused, aggregated, structured, accumulated, and converted to information. The information generated by the data conditioning step feeds into a host of supervised and unsupervised algorithms such as neural networks, which extract patterns, predict new events, fill in missing data, or look for similarities across datasets, thereby converting the input information to actionable knowledge. This actionable knowledge is then passed to human beings for decision-making processes in the human-machine teaming phase. The phase of human-machine teaming provides the users with useful and relevant insight turning knowledge into actionable intelligence or insight.Underlying all of these phases is a bedrock of modern computing systems that is comprised of one or more heterogenous computing elements. For example, sensor processing may occur on low power embedded computers, while algorithms may be computed in very large data centers. With regard to performance advances in these computing elements, Moore's law trends have ended [1], as have a number of related laws and trends including Denard's scaling (power density), clock frequency, core counts, instructions per clock cycle, and instructions per Joule (Koomey's law) [2]. Many of the technologies, tricks and techniques of processor chip designers that

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