1k citations · 1.2k across the 14 of their papers we have counts for
15 papers
Pushing the Limits of Block Rotations in Post-Training Quantization
Sai Sanjeet, Ian Colbert, Pablo Monteagudo-Lago +3
Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of online full-vector rotations,…
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…
A2Q+: Improving Accumulator-Aware Weight Quantization
Ian Colbert, Alessandro Pappalardo, Jakoba Petri-Koenig +1
Quantization techniques commonly reduce the inference costs of neural networks by restricting the precision of weights and activations. Recent studies show that also reducing the p…
QONNX: Representing Arbitrary-Precision Quantized Neural Networks
Alessandro Pappalardo, Yaman Umuroglu, Michaela Blott +11
We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce s…
Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
Hendrik Borras, Giuseppe Di Guglielmo, Javier Duarte +15
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml an…
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Lois Orosa, Skanda Koppula, Yaman Umuroglu +5
Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applicat…
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
Benjamin Hawks, Javier Duarte, Nicholas J. Fraser +3
Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data…
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications
Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser +1
Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficie…
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…
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic
Michaela Blott, Thomas B. Preusser, Nicholas Fraser +4
Convolutional Neural Networks have dramatically improved in recent years, surpassing human accuracy on certain problems and performance exceeding that of traditional computer visio…
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…
Streamlined Deployment for Quantized Neural Networks
Yaman Umuroglu, Magnus Jahre
Running Deep Neural Network (DNN) models on devices with limited computational capability is a challenge due to large compute and memory requirements. Quantized Neural Networks (QN…
Scaling Binarized Neural Networks on Reconfigurable Logic
Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella +4
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suit…
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella +4
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced…