Search Results for author:
Found 13 papers, 6 papers with code
NonGEMM Bench: Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads
Therefore, significant effort has been put to study and optimize the GEMM operators in order to speed up the execution of ML models.
Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers
In this work, we study the effectiveness of existing sparse training recipes at \textit{high-sparsity regions} and argue that these methods fail to sustain the model quality on par with low-sparsity regions.
JaxPruner: A concise library for sparsity research
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research.
Demystifying Map Space Exploration for NPUs
Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model on an accelerator.
Training Recipe for N:M Structured Sparsity with Decaying Pruning Mask
In this work, we focus on N:M sparsity and extensively study and evaluate various training recipes for N:M sparsity in terms of the trade-off between model accuracy and compute cost (FLOPs).
DiGamma: Domain-aware Genetic Algorithm for HW-Mapping Co-optimization for DNN Accelerators
The design of DNN accelerators includes two key parts: HW resource configuration and mapping strategy.
DNNFuser: Generative Pre-Trained Transformer as a Generalized Mapper for Layer Fusion in DNN Accelerators
Dataflow/mapping decides the compute and energy efficiency of DNN accelerators.
FLAT: An Optimized Dataflow for Mitigating Attention Bottlenecks
In contrast, FLAT unblocks transformer models for inputs with up to 64K elements
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores
In particular, we focus on the problem of mapping jobs from several DNNs simultaneously on an accelerator.
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning
We also augment the RL approach with a genetic algorithm for further fine-tuning.
Conditional Neural Architecture Search
Designing resource-efficient Deep Neural Networks (DNNs) is critical to deploy deep learning solutions over edge platforms due to diverse performance, power, and memory budgets.
Generative Design of Hardware-aware DNNs
We propose a new way for autonomous quantization and HW-aware tuning.
Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance.
