2 citations · 2 across the 2 of their papers we have counts for
7 papers
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…