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Found 9 papers, 3 papers with code
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse sparsity degrees.
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.
Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling
This paper first presents a unified taxonomy to systematically describe the diverse sparse tensor accelerator design space.
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.
Union: A Unified HW-SW Co-Design Ecosystem in MLIR for Evaluating Tensor Operations on Spatial Accelerators
The algorithms and accelerator cost models are connected via a novel mapping abstraction that captures the map space of spatial accelerators which can be systematically pruned based on constraints from the hardware, workload, and mapper.
Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search
The key idea is to derive a smooth, differentiable approximation to the otherwise non-smooth, non-convex search space.
Marvel: A Data-centric Compiler for DNN Operators on Spatial Accelerators
Searching for the optimal mappings is challenging because of the large space of mappings, and this challenge gets exacerbated with new operators and diverse accelerator configurations. To address this challenge, we propose a decoupled off-chip/on-chip approach that decomposes the mapping space into off-chip and on-chip subspaces, and first optimizes the off-chip subspace followed by the on-chip subspace.
Understanding Reuse, Performance, and Hardware Cost of DNN Dataflows: A Data-Centric Approach Using MAESTRO
The data partitioning and scheduling strategies used by DNN accelerators to leverage reuse and perform staging are known as dataflow, and they directly impact the performance and energy efficiency of DNN accelerator designs.
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning.
