8 citations · 9 across the 5 of their papers we have counts for
7 papers
GSA to HDL: Towards principled generation of dynamically scheduled circuits
Aditya Rajagopal, Diederik Adriaan Vink, Jianyi Cheng +1
High-level synthesis (HLS) refers to the automatic translation of a software program written in a high-level language into a hardware design. Modern HLS tools have moved away from…
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
Aditya Rajagopal, Christos-Savvas Bouganis
With the increased deployment of Convolutional Neural Networks (CNNs) on edge devices, the uncertainty of the observed data distribution upon deployment has led researchers to to u…
Convolutional Gated MLP: Combining Convolutions & gMLP
A. Rajagopal, V. Nirmala
To the best of our knowledge, this is the first paper to introduce Convolutions to Gated MultiLayer Perceptron and contributes an implementation of this novel Deep Learning archite…
perf4sight: A toolflow to model CNN training performance on Edge GPUs
Aditya Rajagopal, Christos-Savvas Bouganis
The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence. In the domain of vision, the ability to adapt a Convolu…
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
Diederik Adriaan Vink, Aditya Rajagopal, Stylianos I. Venieris +1
As the complexity of deep learning (DL) models increases, their compute requirements increase accordingly. Deploying a Convolutional Neural Network (CNN) involves two phases: train…
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs
Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris +1
Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning…