26 citations · 63 across the 5 of their papers we have counts for
6 papers
AutoML for Multilayer Perceptron and FPGA Co-design
Philip Colangelo, Oren Segal, Alex Speicher +1
State-of-the-art Neural Network Architectures (NNAs) are challenging to design and implement efficiently in hardware. In the past couple of years, this has led to an explosion in r…
Evolutionary Cell Aided Design for Neural Network Architectures
Philip Colangelo, Oren Segal, Alexander Speicher +1
Mathematical theory shows us that multilayer feedforward Artificial Neural Networks(ANNs) are universal function approximators, capable of approximating any measurable function to…
Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs
Philip Colangelo, Nasibeh Nasiri, Asit Mishra +3
CNNs have been shown to maintain reasonable classification accuracy when quantized to lower precisions. Quantizing to sub 8-bit activations and weights can result in accuracy falli…
A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems
Oren Segal, Nasibeh Nasiri, Martin Margala
Heterogeneous computing can potentially offer significant performance and performance per watt improvements over homogeneous computing, but the question "what is the ideal mapping…
SparkCL: A Unified Programming Framework for Accelerators on Heterogeneous Clusters
Oren Segal, Philip Colangelo, Nasibeh Nasiri +2
We introduce SparkCL, an open source unified programming framework based on Java, OpenCL and the Apache Spark framework. The motivation behind this work is to bring unconventional…
High Level Programming for Heterogeneous Architectures
Oren Segal, Martin Margala, Sai Rahul Chalamalasetti +1
This work presents an effort to bridge the gap between abstract high level programming and OpenCL by extending an existing high level Java programming framework (APARAPI), based on…