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Found 6 papers, 2 papers with code
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators
We present novel optimizations based on the above observations to reduce the FP arithmetic hardware overheads.
Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators
This paper studies structured sparse training of CNNs with a gradual pruning technique that leads to fixed, sparse weight matrices after a set number of epochs.
Starfire: Regularization-Free Adversarially-Robust Structured Sparse Training
We show that our method creates a sparse version of ResNet50 and ResNet50v1. 5 on full ImageNet while remaining within a negligible <1% margin of accuracy loss.
CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks
Our results suggest that smaller networks favor non-batched techniques while performance for larger networks is higher using batched operations.
A Systematic Approach to Blocking Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are the state of the art solution for many computer vision problems, and many researchers have explored optimized implementations.
EIE: Efficient Inference Engine on Compressed Deep Neural Network
EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1. 88x10^4 frames/sec with a power dissipation of only 600mW.
