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Found 8 papers, 1 papers with code
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.
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
As a result, our proposed method represents a leap forward in neural network design optimization, paving the way for quick model design and implementation in settings with limited resources, thereby propelling the potential of scalable deep learning solutions.
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation
Post-training compression techniques such as pruning and quantization can help lower deployment costs.
A Fast Training-Free Compression Framework for Vision Transformers
Token pruning has emerged as an effective solution to speed up the inference of large Transformer models.
Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis
Recent efforts for improving the performance of neural network (NN) accelerators that meet today's application requirements have given rise to a new trend of logic-based NN inference relying on fixed function combinational logic.
Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators
This paper introduces the sparse periodic systolic (SPS) dataflow, which advances the state-of-the-art hardware accelerator for supporting lightweight neural networks.
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic
While there is a large body of research on efficient processing of deep neural networks (DNNs), ultra-low-latency realization of these models for applications with stringent, sub-microsecond latency requirements continues to be an unresolved, challenging problem.
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning
The proposed hybrid machine learning model has the same level of accuracy (i. e. $\pm$1%) as NNs while achieving at least 10% improvement in accuracy compared to HD learning models.
