7 documents found, page 1 of 1
Weightless neural networks for efficient edge inference
Susskind, Z.; Arora, A.; Miranda, I. D. S.; Villon, L. A. Q.; Katopodis, R. F.; Araújo, L. S.; Dutra, D. L. C.; Lima, P. M. V.; França, F. M. G.Weightless neural networks (WNNs) are a class of machine learning model which use table lookups to perform inference, rather than the multiply-accumulate operations typical of deep neural networks (DNNs). Individual weightless neurons are capable of learning non-linear functions of their inputs, a theoretical advantage over the linear neurons in DNNs, yet state-of-the-art WNN architectures still lag behind DNNs...
An FPGA-based weightless neural network for edge network intrusion detection
Susskind, Z.; Arora, A.; Bacellar, A.; Dutra, D. L. C.; Miranda, I. D. S.; Breternitz Jr., M.; Lima, P. M. V.; França, F. M. G.; John, L. K.The last decade has seen an explosion in the number of networked edge and Internet-of-Things (IoT) devices, a trend which shows no signs of slowing. Concurrently, networking is increasingly moving away from centralized cloud servers and towards base stations and the edge devices themselves, with the objective of decreasing latency and improving the user experience. ASICs typically lack the flexibility needed to...
COIN: Combinational Intelligent Networks
Miranda, I. D. S.; Arora, A.; Susskind, Z.; Souza, J. S. A.; Jadhao, M. P.; Villon, L. A. Q.; Dutra, D. L. C.; Lima, P. M. V.; França, F. M. G.We introduce Combinational Intelligent Networks (COIN), a machine learning technique that targets edge inference using low-resourced FPGAs or ASICs. COIN is an improvement on LogicWiSARD, a recent weightless neural network that achieves low power, small area, and high throughput. We convert the LogicWiSARD model into a binary neural network, train it using backpropagation, and then convert it to a COIN model. A...
ULEEN: A novel architecture for ultra low-energy edge neural networks
Susskind, Z.; Arora, A.; Miranda, I. D. S.; Bacellar, A. T. L.; Villon, L. A. Q.; Katopodis, R. F.; Araújo, L. S. de; Dutra, D. L. C.; Lima, P. M. V. L."Extreme edge"1 devices, such as smart sensors, are a uniquely challenging environment for the deployment of machine learning. The tiny energy budgets of these devices lie beyond what is feasible for conventional deep neural networks, particularly in high-throughput scenarios, requiring us to rethink how we approach edge inference. In this work, we propose ULEEN, a model and FPGA-based accelerator architecture ...
Pruning weightless neural networks
Susskind, Z.; Bacellar, A. T. L.; Arora, A.; Villon, L. A. Q.; Mendanha, R.; Araújo, L. S. de.; Dutra, D. L. C.; Lima, P. M. V.; França, F. M. G.Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs). Modern DNNs leverage “pruning” techniques to reduce model size, but this has not previously been explored for WNNs. ...
LogicWiSARD: Memoryless synthesis of weightless neural networks
Miranda, I. D. S.; Arora, A.; Susskind, Z.; Villon, L. A. Q.; Katopodis, R. F.; Dutra, D. L. C.; Araújo, L. S. de.; Lima, P. M. V.; França, F. M. G.Weightless neural networks (WNNs) are an alternative pattern recognition technique where RAM nodes function as neurons. As both training and inference require mostly table lookups, few additions, and no multiplications, WNNs are suitable for high-performance and low-power embedded applications. This work introduces a novel approach to implement WiSARD, the leading WNN state-of-the-art architecture, completely e...
GeantV: From CPU to accelerators
Amadio, G. [UNESP]; Ananya, A.; Apostolakis, J.; Arora, A.; Bandieramonte, M.; Bhattacharyya, A.; Bianchini, C. [UNESP]; Brun, R.; Canal, P.Made available in DSpace on 2018-12-11T16:44:53Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-11-21; The GeantV project aims to research and develop the next-generation simulation software describing the passage of particles through matter. While the modern CPU architectures are being targeted first, resources such as GPGPU, Intel © Xeon Phi, Atom or ARM cannot be ignored anymore by HEP CPU-bound appli...