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Found 11 papers, 4 papers with code
A Configurable and Efficient Memory Hierarchy for Neural Network Hardware Accelerator
In this paper, we propose a configurable memory hierarchy framework tailored for per layer adaptive memory access patterns of DNNs.
Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators
In this paper, we demonstrate how to use the ACADL to model AI hardware accelerators, use their ACADL description to map DNNs onto them, and explain the timing simulation semantics to gather performance results.
Precise localization within the GI tract by combining classification of CNNs and time-series analysis of HMMs
This paper presents a method to efficiently classify the gastroenterologic section of images derived from Video Capsule Endoscopy (VCE) studies by exploring the combination of a Convolutional Neural Network (CNN) for classification with the time-series analysis properties of a Hidden Markov Model (HMM).
Collective PV-RCNN: A Novel Fusion Technique using Collective Detections for Enhanced Local LiDAR-Based Perception
Collective Perception (CP) aims to mitigate these problems by enabling the exchange of information between vehicles.
Hardware Accelerator and Neural Network Co-Optimization for Ultra-Low-Power Audio Processing Devices
The increasing spread of artificial neural networks does not stop at ultralow-power edge devices.
HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness
From these results, a validity-driven initialization method for AutoTVM is developed, only requiring 41. 6% of the necessary hardware measurements to find the best solution, while improving search robustness.
Behavior of Keyword Spotting Networks Under Noisy Conditions
Keyword spotting (KWS) is becoming a ubiquitous need with the advancement in artificial intelligence and smart devices.
If your data distribution shifts, use self-learning
We demonstrate that self-learning techniques like entropy minimization and pseudo-labeling are simple and effective at improving performance of a deployed computer vision model under systematic domain shifts.
Ranked #1 on
Unsupervised Domain Adaptation
on ImageNet-A
(using extra training data)
Improving robustness against common corruptions by covariate shift adaptation
With the more robust DeepAugment+AugMix model, we improve the state of the art achieved by a ResNet50 model up to date from 53. 6% mCE to 45. 4% mCE.
Ranked #4 on
Unsupervised Domain Adaptation
on ImageNet-R
A simple way to make neural networks robust against diverse image corruptions
The human visual system is remarkably robust against a wide range of naturally occurring variations and corruptions like rain or snow.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Ranked #1 on
Robust Object Detection
on MS COCO
