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.…
Learning Label Encodings for Deep Regression
Deval Shah, Tor M. Aamodt
Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have…
Label Encoding for Regression Networks
Deval Shah, Zi Yu Xue, Tor M. Aamodt
Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regress…
Rotation-inspired circuit cut optimization
Gideon Uchehara, Tor M. Aamodt, Olivia Di Matteo
Recent works have demonstrated that large quantum circuits can be cut and decomposed into smaller clusters of quantum circuits with fewer qubits that can be executed independently…
Characterizing and Improving the Resilience of Accelerators in Autonomous Robots
Deval Shah, Zi Yu Xue, Karthik Pattabiraman +1
Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for…
Sparse Weight Activation Training
Md Aamir Raihan, Tor M. Aamodt
Neural network training is computationally and memory intensive. Sparse training can reduce the burden on emerging hardware platforms designed to accelerate sparse computations, bu…
Hash-Based Ray Path Prediction: Skipping BVH Traversal Computation by Exploiting Ray Locality
Francois Demoullin, Ayub Gubran, Tor Aamodt
State-of-the-art ray tracing techniques operate on hierarchical acceleration structures such as BVH trees which wrap objects in a scene into bounding volumes of decreasing sizes. A…
Surface Compression Using Dynamic Color Palettes
Ayub A. Gubran, Felix Huang, Tor M. Aamodt
Off-chip memory traffic is a major source of power and energy consumption on mobile platforms. A large amount of this off-chip traffic is used to manipulate graphics framebuffer su…
Analyzing Machine Learning Workloads Using a Detailed GPU Simulator
Jonathan Lew, Deval Shah, Suchita Pati +8
Most deep neural networks deployed today are trained using GPUs via high-level frameworks such as TensorFlow and PyTorch. This paper describes changes we made to the GPGPU-Sim simu…
Modeling Deep Learning Accelerator Enabled GPUs
Md Aamir Raihan, Negar Goli, Tor Aamodt
The efficacy of deep learning has resulted in its use in a growing number of applications. The Volta graphics processor unit (GPU) architecture from NVIDIA introduced a specialized…
Exploring Modern GPU Memory System Design Challenges through Accurate Modeling
Mahmoud Khairy, Jain Akshay, Tor Aamodt +1
This paper explores the impact of simulator accuracy on architecture design decisions in the general-purpose graphics processing unit (GPGPU) space. We perform a detailed, quantita…
HoLiSwap: Reducing Wire Energy in L1 Caches
Yatish Turakhia, Subhasis Das, Tor M. Aamodt +1
This paper describes HoLiSwap a method to reduce L1 cache wire energy, a significant fraction of total cache energy, by swapping hot lines to the cache way nearest to the processor…
CG-OoO: Energy-Efficient Coarse-Grain Out-of-Order Execution
Milad Mohammadi, Tor M. Aamodt, William J. Dally
We introduce the Coarse-Grain Out-of-Order (CG- OoO) general purpose processor designed to achieve close to In-Order processor energy while maintaining Out-of-Order (OoO) performan…
Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets
Patrick Judd, Jorge Albericio, Tayler Hetherington +4
This work investigates how using reduced precision data in Convolutional Neural Networks (CNNs) affects network accuracy during classification. More specifically, this study consid…