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Found 19 papers, 4 papers with code
TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction
Through empirical studies, TrajectoryNAS demonstrates its effectiveness in enhancing the performance of autonomous driving systems, marking a significant advancement in the field. Experimental results reveal that TrajcetoryNAS yield a minimum of 4. 8 higger accuracy and 1. 1* lower latency over competing methods on the NuScenes dataset.
AdAM: Adaptive Fault-Tolerant Approximate Multiplier for Edge DNN Accelerators
In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators.
SAFFIRA: a Framework for Assessing the Reliability of Systolic-Array-Based DNN Accelerators
Systolic array has emerged as a prominent architecture for Deep Neural Network (DNN) hardware accelerators, providing high-throughput and low-latency performance essential for deploying DNNs across diverse applications.
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
The stringent requirements for the Deep Neural Networks (DNNs) accelerator's reliability stand along with the need for reducing the computational burden on the hardware platforms, i. e. reducing the energy consumption and execution time as well as increasing the efficiency of DNN accelerators.
Contrastive Learning for Lane Detection via Cross-Similarity
CLLD is a novel multitask contrastive learning that trains lane detection approaches to detect lane markings even in low visible situations by integrating local feature contrastive learning (CL) with our new proposed operation cross-similarity.
Ranked #4 on
Lane Detection
on TuSimple
FARMUR: Fair Adversarial Retraining to Mitigate Unfairness in Robustness
Finally, we proposed a fair adversarial retraining method (FARMUR) to mitigate unfairness in robustness that retrains the DNN models based on vulnerable and robust sub-partitions.
Enhancing Fault Resilience of QNNs by Selective Neuron Splitting
Thereafter, a novel method for splitting the critical neurons is proposed that enables the design of a Lightweight Correction Unit (LCU) in the accelerator without redesigning its computational part.
Special Session: Approximation and Fault Resiliency of DNN Accelerators
We propose to use approximate (AxC) arithmetic circuits to agilely emulate errors in hardware without performing fault injection on the DNN.
APPRAISER: DNN Fault Resilience Analysis Employing Approximation Errors
Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns.
A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks
Through this SLR, three kinds of methods for reliability assessment of DNNs are identified including Fault Injection (FI), Analytical, and Hybrid methods.
DeepAxe: A Framework for Exploration of Approximation and Reliability Trade-offs in DNN Accelerators
The framework enables selective approximation of reliability-critical DNNs, providing a set of Pareto-optimal DNN implementation design space points for the target resource utilization requirements.
DeepVigor: Vulnerability Value Ranges and Factors for DNNs' Reliability Assessment
In this work, we propose a novel accurate, fine-grain, metric-oriented, and accelerator-agnostic method called DeepVigor that provides vulnerability value ranges for DNN neurons' outputs.
Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search
This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF), a pathological characteristic of Electrocardiogram (ECG) that can lead to fatal conditions such as heart attack.
GTFLAT: Game Theory Based Add-On For Empowering Federated Learning Aggregation Techniques
GTFLAT, as a game theory-based add-on, addresses an important research question: How can a federated learning algorithm achieve better performance and training efficiency by setting more effective adaptive weights for averaging in the model aggregation phase?
DASS: Differentiable Architecture Search for Sparse neural networks
In this paper, we propose a new method to search for sparsity-friendly neural architectures.
TAS: Ternarized Neural Architecture Search for Resource-Constrained Edge Devices
This paper proposes TAS, a framework that drastically reduces the accuracy gap between TNNs and their full-precision counterparts by integrating quantization into the network design.
MuBiNN: Multi-Level Binarized Recurrent Neural Network for EEG signal Classification
Recurrent Neural Networks (RNN) are widely used for learning sequences in applications such as EEG classification.
Multi-level Binarized LSTM in EEG Classification for Wearable Devices
Long Short-Term Memory (LSTM) is widely used in various sequential applications.
Computing with hardware neurons: spiking or classical? Perspectives of applied Spiking Neural Networks from the hardware side
If spike-driven applications, minimizing an amount of spikes, are developed, spiking neural systems may reach the energy efficiency level of classical neural systems.
