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Found 8 papers, 3 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.
GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM
Key-value (KV) caching has become the de-facto to accelerate generation speed for large language models (LLMs) inference.
Algorithm-Hardware Co-Design of Distribution-Aware Logarithmic-Posit Encodings for Efficient DNN Inference
Traditional Deep Neural Network (DNN) quantization methods using integer, fixed-point, or floating-point data types struggle to capture diverse DNN parameter distributions at low precision, and often require large silicon overhead and intensive quantization-aware training.
VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs
Therefore, as DL workloads embrace sparsity to reduce the computations and memory size of models, it is also imperative for CPUs to add support for sparsity to avoid under-utilization of the dense matrix engine and inefficient usage of the caches and registers.
RASA: Efficient Register-Aware Systolic Array Matrix Engine for CPU
As AI-based applications become pervasive, CPU vendors are starting to incorporate matrix engines within the datapath to boost efficiency.
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.
Evaluating Spatial Accelerator Architectures with Tiled Matrix-Matrix Multiplication
There is a growing interest in custom spatial accelerators for machine learning applications.
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.
