67 research outputs found
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing
computing efficiency and capabilities by following brain-inspired principles.
However, the rich diversity of techniques employed in neuromorphic research has
resulted in a lack of clear standards for benchmarking, hindering effective
evaluation of the advantages and strengths of neuromorphic methods compared to
traditional deep-learning-based methods. This paper presents a collaborative
effort, bringing together members from academia and the industry, to define
benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are
to be a collaborative, fair, and representative benchmark suite developed by
the community, for the community. In this paper, we discuss the challenges
associated with benchmarking neuromorphic solutions, and outline the key
features of NeuroBench. We believe that NeuroBench will be a significant step
towards defining standards that can unify the goals of neuromorphic computing
and drive its technological progress. Please visit neurobench.ai for the latest
updates on the benchmark tasks and metrics
NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics
Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications
Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications
Sub-mW Neuromorphic SNN audio processing applications with Rockpool and Xylo
Spiking Neural Networks (SNNs) provide an efficient computational mechanism
for temporal signal processing, especially when coupled with low-power SNN
inference ASICs. SNNs have been historically difficult to configure, lacking a
general method for finding solutions for arbitrary tasks. In recent years,
gradient-descent optimization methods have been applied to SNNs with increasing
ease. SNNs and SNN inference processors therefore offer a good platform for
commercial low-power signal processing in energy constrained environments
without cloud dependencies. However, to date these methods have not been
accessible to ML engineers in industry, requiring graduate-level training to
successfully configure a single SNN application. Here we demonstrate a
convenient high-level pipeline to design, train and deploy arbitrary temporal
signal processing applications to sub-mW SNN inference hardware. We apply a new
straightforward SNN architecture designed for temporal signal processing, using
a pyramid of synaptic time constants to extract signal features at a range of
temporal scales. We demonstrate this architecture on an ambient audio
classification task, deployed to the Xylo SNN inference processor in streaming
mode. Our application achieves high accuracy (98%) and low latency (100ms) at
low power (<100W inference power). Our approach makes training and
deploying SNN applications available to ML engineers with general NN
backgrounds, without requiring specific prior experience with spiking NNs. We
intend for our approach to make Neuromorphic hardware and SNNs an attractive
choice for commercial low-power and edge signal processing applications.Comment: This submission has been removed by arXiv administrators because the
submitter did not have the authority to grant a license to the work at the
time of submissio
Benchmarking of Neuromorphic Hardware Systems
Ostrau C, Klarhorst C, Thies M, Rückert U. Benchmarking of Neuromorphic Hardware Systems. In: Neuro-inspired Computational Elements Workshop (NICE ’20), March 17–20, 2020, Heidelberg, Germany. International Conference Proceeding Series (ICPS). Association for Computing Machinery (ACM); 2020.With more and more neuromorphic hardware systems for the accel-
eration of spiking neural networks available in science and industry,
there is a demand for platform comparison and performance esti-
mation of such systems. This work describes selected benchmarks
implemented in a framework with exactly this target: independent
black-box benchmarking and comparison of platforms suitable for
the simulation/emulation of spiking neural networks
A Construction Kit for Efficient Low Power Neural Network Accelerator Designs
Implementing embedded neural network processing at the edge requires
efficient hardware acceleration that couples high computational performance
with low power consumption. Driven by the rapid evolution of network
architectures and their algorithmic features, accelerator designs are
constantly updated and improved. To evaluate and compare hardware design
choices, designers can refer to a myriad of accelerator implementations in the
literature. Surveys provide an overview of these works but are often limited to
system-level and benchmark-specific performance metrics, making it difficult to
quantitatively compare the individual effect of each utilized optimization
technique. This complicates the evaluation of optimizations for new accelerator
designs, slowing-down the research progress. This work provides a survey of
neural network accelerator optimization approaches that have been used in
recent works and reports their individual effects on edge processing
performance. It presents the list of optimizations and their quantitative
effects as a construction kit, allowing to assess the design choices for each
building block separately. Reported optimizations range from up to 10'000x
memory savings to 33x energy reductions, providing chip designers an overview
of design choices for implementing efficient low power neural network
accelerators
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