22 citations · 29 across the 7 of their papers we have counts for
7 papers · 1 filter
Arithmetic Packing on Wide Integer Datapaths in DSP Primitives of Modern FPGA Devices
Titus Bornträger, Shane Fleming, Philipp Holzinger +3
Deep Neural Networks increasingly employ low-precision quantization to reduce computational requirements. While FPGAs are well suited for workloads with heterogeneous precisions, t…
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…
MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions
Wenqi Jiang, Zhenhao He, Shuai Zhang +9
Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random…
Using DSP Slices as Content-Addressable Update Queues
Thomas B. Preußer, Monica Chiosa, Alexander Weiss +1
Content-Addressable Memory (CAM) is a powerful abstraction for building memory caches, routing tables and hazard detection logic. Without a native CAM structure available on FPGA d…
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…