365 citations · 471 across the 17 of their papers we have counts for
19 papers
Quantization-Aware Pre-Training with Constrained Empirical Weight Distribution
Ningfeng Yang, Tor M. Aamodt
Quantization-Aware Pre-Training (QAPT) can increase the inference efficiency of DNNs, but a problematic behaviour known as rounding boundary weight oscillation can introduce detrim…
Boosting Entropy with Bell Box Quantization
Ningfeng Yang, Tor M. Aamodt
Quantization-Aware Pre-Training (QAPT) is an effective technique to reduce the compute and memory overhead of Deep Neural Networks while improving their energy efficiency on edge d…
RoboGPU: Accelerating GPU Collision Detection for Robotics
Lufei Liu, Liwei Xue, Youssef Mohammed +4
Autonomous robots are anticipated to be deployed soon in domains ranging from transportation to healthcare and home assistance. Enabling autonomous robotics requires a computation…
Improving the Straight-Through Estimator with Zeroth-Order Information
Ningfeng Yang, Tor M. Aamodt
We study the problem of training neural networks with quantized parameters. Learning low-precision quantized parameters by enabling computation of gradients via the Straight-Throug…
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering
Lufei Liu, Tor M. Aamodt
Graphics rendering applications increasingly leverage neural networks in tasks such as denoising, supersampling, and frame extrapolation to improve image quality while maintaining…
Graph-based identification of qubit network (GidNET) for qubit reuse
Gideon Uchehara, Tor M. Aamodt, Olivia Di Matteo
Quantum computing introduces the challenge of optimizing quantum resources crucial for executing algorithms within the limited qubit availability of current quantum architectures.…