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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 …
techniques, modern DNNs can have weights and activations that are dense or sparse with …
CiMLoop: A flexible, accurate, and fast compute-in-memory modeling tool
Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks
(DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to …
(DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to …
Mind the gap: Attainable data movement and operational intensity bounds for tensor algorithms
The architectural design-space exploration (or DSE) process-whether manual or automated-
benefits greatly from knowing the limits of the metrics of interest in advance. Data movement …
benefits greatly from knowing the limits of the metrics of interest in advance. Data movement …
Demystifying map space exploration for npus
Map Space Exploration is the problem of finding optimized map**s of a Deep Neural
Network (DNN) model on an accelerator. It is known to be extremely computationally …
Network (DNN) model on an accelerator. It is known to be extremely computationally …
Architecture-level modeling of photonic deep neural network accelerators
Photonics is a promising technology to accelerate Deep Neural Networks as it can use
optical interconnects to reduce data movement energy and it enables low-energy, high …
optical interconnects to reduce data movement energy and it enables low-energy, high …
Ceiba: An Efficient and Scalable DNN Scheduler for Spatial Accelerators
F Wang, M Shen, Y Lu, N ** Framework for Processing In-Memory Neural Network Acceleration
Processing in-memory (PIM) is promising to accelerate neural networks (NNs) because it
minimizes data movement and provides large computational parallelism. Similar to machine …
minimizes data movement and provides large computational parallelism. Similar to machine …
DNNOPT: A Framework for Efficiently Selecting On-chip Memory Loop Optimizations of DNN Accelerators
Deep neural network (DNN) accelerators suffer from poor utilization of on-chip memory
which potentially reduces performance and energy efficiency. Loop reordering and blocking …
which potentially reduces performance and energy efficiency. Loop reordering and blocking …