5 citations · 8 across the 9 of their papers we have counts for
9 papers
On Calibration of Modern Quantized Efficient Neural Networks
Joey Kuang, Alexander Wong
We explore calibration properties at various precisions for three architectures: ShuffleNetv2, GhostNet-VGG, and MobileOne; and two datasets: CIFAR-100 and PathMNIST. The quality o…
NAS-NeRF: Generative Neural Architecture Search for Neural Radiance Fields
Saeejith Nair, Yuhao Chen, Mohammad Javad Shafiee +1
Neural radiance fields (NeRFs) enable high-quality novel view synthesis, but their high computational complexity limits deployability. While existing neural-based solutions strive…
TurboViT: Generating Fast Vision Transformers via Generative Architecture Search
Alexander Wong, Saad Abbasi, Saeejith Nair
Vision transformers have shown unprecedented levels of performance in tackling various visual perception tasks in recent years. However, the architectural and computational complex…
Systematic Architectural Design of Scale Transformed Attention Condenser DNNs via Multi-Scale Class Representational Response Similarity Analysis
Andre Hryniowski, Alexander Wong
Self-attention mechanisms are commonly included in a convolutional neural networks to achieve an improved efficiency performance balance. However, adding self-attention mechanisms…
Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge
Alexander Wong, Yifan Wu, Saad Abbasi +3
Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architect…
Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers
Alexander Wong, Mohammad Javad Shafiee, Saad Abbasi +2
With the growing adoption of deep learning for on-device TinyML applications, there has been an ever-increasing demand for efficient neural network backbones optimized for the edge…
MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge
Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee
Deep neural network (DNN) latency characterization is a time-consuming process and adds significant cost to Neural Architecture Search (NAS) processes when searching for efficient…
MAPLE-Edge: A Runtime Latency Predictor for Edge Devices
Saeejith Nair, Saad Abbasi, Alexander Wong +1
Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although re…
MAPLE: Microprocessor A Priori for Latency Estimation
Saad Abbasi, Alexander Wong, Mohammad Javad Shafiee
Modern deep neural networks must demonstrate state-of-the-art accuracy while exhibiting low latency and energy consumption. As such, neural architecture search (NAS) algorithms tak…