184 research outputs found
Fast Reactive Power Sharing, Circulating Current and Resonance Suppression for Parallel Inverters Using Resistive-Capacitive Output Impedance
Fast-HuBERT: An Efficient Training Framework for Self-Supervised Speech Representation Learning
Recent years have witnessed significant advancements in self-supervised
learning (SSL) methods for speech-processing tasks. Various speech-based SSL
models have been developed and present promising performance on a range of
downstream tasks including speech recognition. However, existing speech-based
SSL models face a common dilemma in terms of computational cost, which might
hinder their potential application and in-depth academic research. To address
this issue, we first analyze the computational cost of different modules during
HuBERT pre-training and then introduce a stack of efficiency optimizations,
which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be
trained in 1.1 days with 8 V100 GPUs on the Librispeech 960h benchmark, without
performance degradation, resulting in a 5.2x speedup, compared to the original
implementation. Moreover, we explore two well-studied techniques in the
Fast-HuBERT and demonstrate consistent improvements as reported in previous
work
Robust Grid-Current-Feedback Resonance Suppression Method for LCL-Type Grid-Connected Inverter Connected to Weak Grid
ClickINC: In-network Computing as a Service in Heterogeneous Programmable Data-center Networks
In-Network Computing (INC) has found many applications for performance boosts
or cost reduction. However, given heterogeneous devices, diverse applications,
and multi-path network typologies, it is cumbersome and error-prone for
application developers to effectively utilize the available network resources
and gain predictable benefits without impeding normal network functions.
Previous work is oriented to network operators more than application
developers. We develop ClickINC to streamline the INC programming and
deployment using a unified and automated workflow. ClickINC provides INC
developers a modular programming abstractions, without concerning to the states
of the devices and the network topology. We describe the ClickINC framework,
model, language, workflow, and corresponding algorithms. Experiments on both an
emulator and a prototype system demonstrate its feasibility and benefits
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