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Found 6 papers, 2 papers with code
Abstracting Sparse DNN Acceleration via Structured Sparse Tensor Decomposition
Next, we develop a software framework, TASDER, to accelerate DNNs by searching layer-wise, high-quality structured decomposition for both weight and activation tensors so that they can be accelerated by any systems with structured sparse hardware support.
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.
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.
