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Beyond Accuracy: Enhancing Parkinson's Diagnosis with Uncertainty Quantification of Machine Learning Models. [PDF]
Artif Intell Healthc (2025)Azad A, Islam MS, Hoque E, Rahman MS.
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LP-BNN: Ultra-low-Latency BNN Inference with Layer Parallelism
2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019High inference latency seriously limits the deployment of DNNs in real-time domains such as autonomous driving, robotic control, and many others. To address this emerging challenge, researchers have proposed approximate DNNs with reduced precision, e.g., Binarized Neural Networks (BNNs).
Martin Herbordt +2 more
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A high-throughput scalable BNN accelerator with fully pipelined architecture
CCF Transactions on High Performance Computing, 2021By replacing multiplication with XNOR operation, Binarized Neural Networks (BNN) are hardware-friendly and extremely suitable for FPGA acceleration. Previous researches highlighted the potential exploitation of BNNs performance. However, most of the present researches targeted at minimizing chip areas.
Dong Wen, Zhe Han, Yong Dou
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BNN Training Algorithm with Ternary Gradients and BNN based on MRAM Array
TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), 2023Takayuki Kawahara
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Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data ...
Amit Livne +3 more
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Deep learning (DL) algorithms have played a major role in achieving state-of-the-art (SOTA) performance in various learning applications, including computer vision, natural language processing, and recommendation systems (RSs). However, these methods are based on a vast amount of data and do not perform as well when there is a limited amount of data ...
Amit Livne +3 more
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Journal of Catalysis, 2021
Abstract Porous hexagonal boron nitride nanosheets (p-BNNS) have demonstrated advantages in hydrogen storage, water purification and catalyst support. Boron nitride (BN) is generally considered chemically inert, but functionalized h-BN by physical or chemical methods breed new properties and applications and can catalyze some reactions. Herein, we
Qiong Lu +7 more
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Abstract Porous hexagonal boron nitride nanosheets (p-BNNS) have demonstrated advantages in hydrogen storage, water purification and catalyst support. Boron nitride (BN) is generally considered chemically inert, but functionalized h-BN by physical or chemical methods breed new properties and applications and can catalyze some reactions. Herein, we
Qiong Lu +7 more
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Physically Tightly Coupled, Logically Loosely Coupled, Near-Memory BNN Accelerator (PTLL-BNN)
ESSCIRC 2019 - IEEE 45th European Solid State Circuits Conference (ESSCIRC), 2019In this paper, a physically tightly coupled, logically loosely coupled, near-memory binary neural network accelerator (PTLL-BNN) is designed and fabricated. Both architecture-level and circuit-level optimizations are presented. From the perspective of processor architecture, the PTLL-BNN includes two new design choices.
Yun-Chen Lo +7 more
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SA-BNN: State-Aware Binary Neural Network
Proceedings of the AAAI Conference on Artificial Intelligence, 2021Binary Neural Networks (BNNs) have received significant attention due to the memory and computation efficiency recently. However, the considerable accuracy gap between BNNs and their full-precision counterparts hinders BNNs to be deployed to resource-constrained platforms.
Chunlei Liu 0001 +5 more
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FPGA Architecture Enhancements for Efficient BNN Implementation
2018 International Conference on Field-Programmable Technology (FPT), 2018Binarized neural networks (BNNs) are ultra-reduced precision neural networks, having weights and activations restricted to single-bit values. BNN computations operate on bitwise data, making them particularly amenable to hardware implementation. In this paper, we first analyze BNN implementations on contemporary commercial 20nm FPGAs.
Jin Hee Kim +2 more
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