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Neural Inspired Computation Remote Sensing Platform

Vineyard, Craig M.; Severa, William M.; Green, Sam G.; Dellana, Ryan A.; Plagge, Mark P.; Hill, Aaron J.

Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.

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TYPE SAND Report YEAR 2019
OSTIDOI

Composing neural algorithms with Fugu

ACM International Conference Proceeding Series

Aimone, James B.; Severa, William M.; Vineyard, Craig M.

Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, these computer platforms can offer significant energy benefits compared to traditional von Neumann processors. Unfortunately there still remains considerable difficulty in successfully programming, configuring and deploying neuromorphic systems. We present the Fugu framework as an answer to this need. Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources. Individual kernels linked together provide sophisticated processing through compositionality. Fugu is intended to be suitable for a wide-range of neuromorphic applications, including machine learning, scientific computing, and more brain-inspired neural algorithms. Ultimately, we hope the community adopts this and other open standardization attempts allowing for free exchange and easy implementations of the ever-growing list of spiking neural algorithms.

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TYPE Conference Poster YEAR 2019
ScopusOSTIDOI

A resurgence in neuromorphic architectures enabling remote sensing computation

Proceedings - 2019 IEEE Space Computing Conference, SCC 2019

Vineyard, Craig M.; Severa, William M.; Kagie, Matthew J.; Scholand, Andrew J.; Hays, Park H.

Technological advances have enabled exponential growth in both sensor data collection, as well as computational processing. However, as a limiting factor, the transmission bandwidth in between a space-based sensor and a ground station processing center has not seen the same growth. A resolution to this bandwidth limitation is to move the processing to the sensor, but doing so faces size, weight, and power operational constraints. Different physical constraints on processor manufacturing are spurring a resurgence in neuromorphic approaches amenable to the space-based operational environment. Here we describe historical trends in computer architecture and the implications for neuromorphic computing, as well as give an overview of how remote sensing applications may be impacted by this emerging direction for computing.

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TYPE Conference Poster YEAR 2019
ScopusOSTIDOI

Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform

ACM International Conference Proceeding Series

Vineyard, Craig M.; Dellana, Ryan A.; Aimone, James B.; Rothganger, Fredrick; Severa, William M.

With the successes deep neural networks have achieved across a range of applications, researchers have been exploring computational architectures to more efficiently execute their operation. In addition to the prevalent role of graphics processing units (GPUs), many accelerator architectures have emerged. Neuromorphic is one such particular approach which takes inspiration from the brain to guide the computational principles of the architecture including varying levels of biological realism. In this paper we present results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia National Laboratories developed Whetstone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.

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TYPE Conference Poster YEAR 2019
ScopusOSTIDOI

Mathematical optimizations for deep learning

Cyber-Physical Systems Security

Green, Sam G.; Vineyard, Craig M.; Koç, Çetin K.

Deep neural networks are often computationally expensive, during both the training stage and inference stage. Training is always expensive, because back-propagation requires high-precision floating-pointmultiplication and addition. However, various mathematical optimizations may be employed to reduce the computational cost of inference. Optimized inference is important for reducing power consumption and latency and for increasing throughput. This chapter introduces the central approaches for optimizing deep neural network inference: pruning "unnecessary" weights, quantizing weights and inputs, sharing weights between layer units, compressing weights before transferring from main memory, distilling large high-performance models into smaller models, and decomposing convolutional filters to reduce multiply and accumulate operations. In this chapter, using a unified notation, we provide a mathematical and algorithmic description of the aforementioned deep neural network inference optimization methods.

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TYPE Book YEAR 2018
ScopusOSTIDOI

Impacts of Mathematical Optimizations on Reinforcement Learning Policy Performance

Proceedings of the International Joint Conference on Neural Networks

Green, Sam G.; Vineyard, Craig M.; Koc, Cetin K.

Deep neural networks (DNN) now outperform competing methods in many academic and industrial domains. These high-capacity universal function approximators have recently been leveraged by deep reinforcement learning (RL) algorithms to obtain impressive results for many control and decision making problems. During the past three years, research toward pruning, quantization, and compression of DNNs has reduced the mathematical, and therefore time and energy, requirements of DNN-based inference. For example, DNN optimization techniques have been developed which reduce storage requirements of VGG-16 from 552MB to 11.3MB, while maintaining the full-model accuracy for image classification. Building from DNN optimization results, the computer architecture community is taking increasing interest in exploring DNN hardware accelerator designs. Based on recent deep RL performance, we expect hardware designers to begin considering architectures appropriate for accelerating these algorithms too. However, it is currently unknown how, when, or if the 'noise' introduced by DNN optimization techniques will degrade deep RL performance. This work measures these impacts, using standard OpenAI Gym benchmarks. Our results show that mathematically optimized RL policies can perform equally to full-precision RL, while requiring substantially less computation. We also observe that different optimizations are better suited than others for different problem domains. By beginning to understand the impacts of mathematical optimizations on RL policy performance, this work serves as a starting point toward the development of low power or high performance deep RL accelerators.

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TYPE Conference Poster YEAR 2018
ScopusOSTIDOI

Computing with spikes: The advantage of fine-grained timing

Neural Computation

Verzi, Stephen J.; Rothganger, Fredrick R.; Parekh, Ojas D.; Quach, Tu-Thach Q.; Miner, Nadine E.; Vineyard, Craig M.; James, Conrad D.; Aimone, James B.

Neural-inspired spike-based computing machines often claim to achieve considerable advantages in terms of energy and time efficiency by using spikes for computation and communication. However, fundamental questions about spike-based computation remain unanswered. For instance, how much advantage do spike-based approaches have over conventionalmethods, and underwhat circumstances does spike-based computing provide a comparative advantage? Simply implementing existing algorithms using spikes as the medium of computation and communication is not guaranteed to yield an advantage. Here, we demonstrate that spike-based communication and computation within algorithms can increase throughput, and they can decrease energy cost in some cases. We present several spiking algorithms, including sorting a set of numbers in ascending/descending order, as well as finding the maximum or minimum ormedian of a set of numbers.We also provide an example application: a spiking median-filtering approach for image processing providing a low-energy, parallel implementation. The algorithms and analyses presented here demonstrate that spiking algorithms can provide performance advantages and offer efficient computation of fundamental operations useful in more complex algorithms.

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TYPE Journal Article YEAR 2018
ScopusOSTIDOI
Results 26–50 of 113
Results 26–50 of 113