New paper on CNN accelerator architectures to appear at ISCA 2017

Our new paper on improving the efficiency of hardware accelerators for convolutional neural networks has been accepted for publication at the 44th International Symposium on Computer Architecture (ISCA), 2017.

This paper, co-authored with Yongming Shen (Stony Brook CS PhD student) and Stony Brook CS professor Mike Ferdman, proposes a new Convolutional Neural Network (CNN) accelerator paradigm and an accompanying automated design methodology that partitions the available FPGA resources into multiple processors, each of which is tailored for a different subset of the CNN convolutional layers.

Yongming Shen, Michael Ferdman, and Peter Milder. “Maximizing CNN Accelerator Efficiency Through Resource Partitioning.” To appear at The 44th International Symposium on Computer Architecture (ISCA), 2017.

You can read a pre-print here.