1 citations · 1 across the 6 of their papers we have counts for
8 papers
Connecting Independently Trained Modes via Layer-Wise Connectivity
Yongding Tian, Zaid Al-Ars, Maksim Kitsak +1
Empirical studies have shown that continuous low-loss paths can be constructed between independently trained neural network models. This phenomenon, known as mode connectivity, ref…
TINA: Acceleration of Non-NN Signal Processing Algorithms Using NN Accelerators
Christiaan Boerkamp, Steven van der Vlugt, Zaid Al-Ars
This paper introduces TINA, a novel framework for implementing non Neural Network (NN) signal processing algorithms on NN accelerators such as GPUs, TPUs or FPGAs. The key to this…
Tywaves: A Typed Waveform Viewer for Chisel
Raffaele Meloni, H. Peter Hofstee, Zaid Al-Ars
Chisel (Constructing Hardware In a Scala Embedded Language) is a broadly adopted HDL that brings object-oriented and functional programming, type-safety, and parameterization to ha…
Leveraging Apache Arrow for Zero-copy, Zero-serialization Cluster Shared Memory
Philip Groet, Joost Hoozemans, Andreas Grapentin +3
This paper describes a distributed implementation of Apache Arrow that can leverage cluster-shared load-store addressable memory that is hardware-coherent only within each node. Th…
Vanishing Variance Problem in Fully Decentralized Neural-Network Systems
Yongding Tian, Zaid Al-Ars, Maksim Kitsak +1
Federated learning and gossip learning are emerging methodologies designed to mitigate data privacy concerns by retaining training data on client devices and exclusively sharing lo…
NASH: Neural Architecture Search for Hardware-Optimized Machine Learning Models
Mengfei Ji, Yuchun Chang, Baolin Zhang +1
As machine learning (ML) algorithms get deployed in an ever-increasing number of applications, these algorithms need to achieve better trade-offs between high accuracy, high throug…