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Found 6 papers, 4 papers with code
Hexcute: A Tile-based Programming Language with Automatic Layout and Task-Mapping Synthesis
Deep learning (DL) workloads mainly run on accelerators like GPUs.
Tilus: A Virtual Machine for Arbitrary Low-Precision GPGPU Computation in LLM Serving
Existing approaches for generating low-precision kernels are limited to weight bit widths that are powers of two and suffer from suboptimal performance due to high-level GPU programming abstractions.
Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs
With the proposed paradigm, we implement a deep learning compiler Hidet.
IOS: Inter-Operator Scheduler for CNN Acceleration
To accelerate CNN inference, existing deep learning frameworks focus on optimizing intra-operator parallelization.
GAN Compression: Efficient Architectures for Interactive Conditional GANs
Directly applying existing compression methods yields poor performance due to the difficulty of GAN training and the differences in generator architectures.
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
