Showing cs.DCShow all
3 papers · 1 filter
cs.DC2018
NTX: An Energy-efficient Streaming Accelerator for Floating-point Generalized Reduction Workloads in 22nm FD-SOI
Fabian Schuiki, Michael Schaffner, Luca Benini
Specialized coprocessors for Multiply-Accumulate (MAC) intensive workloads such as Deep Learning are becoming widespread in SoC platforms, from GPUs to mobile SoCs. In this paper w…
cs.DC2018
On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations
Michael Schaffner, Luca Benini
Classical molecular dynamics (MD) simulations are important tools in life and material sciences since they allow studying chemical and biological processes in detail. However, the…
cs.DC2018
A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets
Fabian Schuiki, Michael Schaffner, Frank K. Gürkaynak +1
Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received r…