Search Results for author:
Found 6 papers, 3 papers with code
MajorityNets: BNNs Utilising Approximate Popcount for Improved Efficiency
Binarized neural networks (BNNs) have shown exciting potential for utilising neural networks in embedded implementations where area, energy and latency constraints are paramount.
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images
In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance.
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
We propose an optimization method for the automatic design of approximate multipliers, which minimizes the average error according to the operand distributions.
Low Error-Rate Approximate Multiplier Design for DNNs with Hardware-Driven Co-Optimization
In this paper, two approximate 3*3 multipliers are proposed and the synthesis results of the ASAP-7nm process library justify that they can reduce the area by 31. 38% and 36. 17%, and the power consumption by 36. 73% and 35. 66% compared with the exact multiplier, respectively.
Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes
We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs.
An Open-source End-to-End Logic Optimization Framework for Large-scale Boolean Network with Reinforcement Learning
We propose an open-source end-to-end logic optimization framework for large-scale boolean network with reinforcement learning.
