1k citations · 1.1k across the 9 of their papers we have counts for
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SIRA: Scaled-Integer Range Analysis for Optimizing FPGA Dataflow Neural Network Accelerators
Yaman Umuroglu, Christoph Berganski, Felix Jentzsch +8
While neural network quantization effectively reduces the cost of matrix multiplications, aggressive quantization can expose non-matrix-multiply operations as significant performan…
Optimizing Bit-Serial Matrix Multiplication for Reconfigurable Computing
Yaman Umuroglu, Davide Conficconi, Lahiru Rasnayake +2
Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to…
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
Michaela Blott, Thomas Preusser, Nicholas Fraser +3
Convolutional Neural Networks have rapidly become the most successful machine learning algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded comp…
BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing
Yaman Umuroglu, Lahiru Rasnayake, Magnus Sjalander
Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to…