activity
20162022
most citedSparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

341 citations · 597 across the 28 of their papers we have counts for

collaborators

74 papers

cs.LG20225 cited

Spatial Mixture-of-Experts

Nikoli Dryden, Torsten Hoefler

Many data have an underlying dependence on spatial location; it may be weather on the Earth, a simulation on a mesh, or a registered image. Yet this feature is rarely taken advanta…

quant-ph202267 cited

Assessing requirements to scale to practical quantum advantage

Michael E. Beverland, Prakash Murali, Matthias Troyer +7

While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require…

cs.DC20221 cited

Noise in the Clouds: Influence of Network Performance Variability on Application Scalability

Daniele De Sensi, Tiziano De Matteis, Konstantin Taranov +3

Cloud computing represents an appealing opportunity for cost-effective deployment of HPC workloads on the best-fitting hardware. However, although cloud and on-premise HPC systems…

cs.DC2022

HammingMesh: A Network Topology for Large-Scale Deep Learning

Torsten Hoefler, Tommaso Bonato, Daniele De Sensi +7

Numerous microarchitectural optimizations unlocked tremendous processing power for deep neural networks that in turn fueled the AI revolution. With the exhaustion of such optimizat…

cs.DC20221 cited

Temporal Vectorization: A Compiler Approach to Automatic Multi-Pumping

Carl-Johannes Johnsen, Tiziano De Matteis, Tal Ben-Nun +2

The multi-pumping resource sharing technique can overcome the limitations commonly found in single-clocked FPGA designs by allowing hardware components to operate at a higher clock…

cs.DC20221 cited

Fast Arbitrary Precision Floating Point on FPGA

Johannes de Fine Licht, Christopher A. Pattison, Alexandros Nikolaos Ziogas +2

Numerical codes that require arbitrary precision floating point (APFP) numbers for their core computation are dominated by elementary arithmetic operations due to the super-linear…