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Found 8 papers, 0 papers with code
Efficient Noise Mitigation for Enhancing Inference Accuracy in DNNs on Mixed-Signal Accelerators
In this paper, we propose a framework to enhance the robustness of the neural models by mitigating the effects of process-induced and aging-related variations of analog computing components on the accuracy of the analog neural networks.
Enhancing Layout Hotspot Detection Efficiency with YOLOv8 and PCA-Guided Augmentation
Additionally, to enhance pattern-matching effectiveness, we introduce a novel approach to augment the layout image using information extracted through Principal Component Analysis (PCA).
ARCO:Adaptive Multi-Agent Reinforcement Learning-Based Hardware/Software Co-Optimization Compiler for Improved Performance in DNN Accelerator Design
This paper presents ARCO, an adaptive Multi-Agent Reinforcement Learning (MARL)-based co-optimizing compilation framework designed to enhance the efficiency of mapping machine learning (ML) models - such as Deep Neural Networks (DNNs) - onto diverse hardware platforms.
Scalable Superconductor Neuron with Ternary Synaptic Connections for Ultra-Fast SNN Hardware
A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented.
Low-Precision Mixed-Computation Models for Inference on Edge
This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (FixP) number systems.
A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks
In this work, to limit the number of required attention inference hops in memory-augmented neural networks, we propose an online adaptive approach called A2P-MANN.
BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification
In this paper, first, a hardware-friendly pruning algorithm for reducing energy consumption and improving the speed of Long Short-Term Memory (LSTM) neural network accelerators is presented.
Space Expansion of Feature Selection for Designing more Accurate Error Predictors
The results on various benchmarks demonstrate significant improvements in the prediction accuracy compared to the prior works which used only the accelerator inputs for the prediction.
