257 citations · 483 across the 39 of their papers we have counts for
8 papers · 1 filter
A Probabilistic Approach to Floating-Point Arithmetic
Fredrik Dahlqvist, Rocco Salvia, George A Constantinides
Finite-precision floating point arithmetic unavoidably introduces rounding errors which are traditionally bounded using a worst-case analysis. However, worst-case analysis might be…
Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs
Yiren Zhao, Xitong Gao, Xuan Guo +6
Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet real applications often require high throughput and low latency. To help tackle these problems,…
LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference
Erwei Wang, James J. Davis, Peter Y. K. Cheung +1
Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quanti…
ARCHITECT: Arbitrary-precision Hardware with Digit Elision for Efficient Iterative Compute
He Li, James J. Davis, John Wickerson +1
Many algorithms feature an iterative loop that converges to the result of interest. The numerical operations in such algorithms are generally implemented using finite-precision ari…
Rethinking Arithmetic for Deep Neural Networks
George A. Constantinides
We consider efficiency in the implementation of deep neural networks. Hardware accelerators are gaining interest as machine learning becomes one of the drivers of high-performance…
LUTNet: Rethinking Inference in FPGA Soft Logic
Erwei Wang, James J. Davis, Peter Y. K. Cheung +1
Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised…