198 research outputs found
Weighted-Sampling Audio Adversarial Example Attack
Recent studies have highlighted audio adversarial examples as a ubiquitous
threat to state-of-the-art automatic speech recognition systems. Thorough
studies on how to effectively generate adversarial examples are essential to
prevent potential attacks. Despite many research on this, the efficiency and
the robustness of existing works are not yet satisfactory. In this paper, we
propose~\textit{weighted-sampling audio adversarial examples}, focusing on the
numbers and the weights of distortion to reinforce the attack. Further, we
apply a denoising method in the loss function to make the adversarial attack
more imperceptible. Experiments show that our method is the first in the field
to generate audio adversarial examples with low noise and high audio robustness
at the minute time-consuming level.Comment: https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuXL.9260.pd
TC-GNN: Accelerating Sparse Graph Neural Network Computation Via Dense Tensor Core on GPUs
Recently, graph neural networks (GNNs), as the backbone of graph-based
machine learning, demonstrate great success in various domains (e.g.,
e-commerce). However, the performance of GNNs is usually unsatisfactory due to
the highly sparse and irregular graph-based operations. To this end, we
propose, TC-GNN, the first GPU Tensor Core Unit (TCU) based GNN acceleration
framework. The core idea is to reconcile the "Sparse" GNN computation with
"Dense" TCU. Specifically, we conduct an in-depth analysis of the sparse
operations in mainstream GNN computing frameworks. We introduce a novel sparse
graph translation technique to facilitate TCU processing of sparse GNN
workload. We also implement an effective CUDA core and TCU collaboration design
to fully utilize GPU resources. We fully integrate TC-GNN with the Pytorch
framework for ease of programming. Rigorous experiments show an average of
1.70X speedup over the state-of-the-art Deep Graph Library framework across
various GNN models and dataset settings
S-QGPU: Shared Quantum Gate Processing Unit for Distributed Quantum Computing
We propose a distributed quantum computing (DQC) architecture in which
individual small-sized quantum computers are connected to a shared quantum gate
processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit
gate modules for remote gate operations. In contrast to conventional DQC
systems, where each quantum computer is equipped with dedicated communication
qubits, S-QGPU effectively pools the resources (e.g., the communication qubits)
together for remote gate operations, and thus significantly reduces the cost of
not only the local quantum computers but also the overall distributed system.
Moreover, S-QGPU's shared resources for remote gate operations enable efficient
resource utilization. When not all computing qubits in the system require
simultaneous remote gate operations, S-QGPU-based DQC architecture demands
fewer communication qubits, further decreasing the overall cost. Alternatively,
with the same number of communication qubits, it can support a larger number of
simultaneous remote gate operations more efficiently, especially when these
operations occur in a burst mode.Comment: 8 pages, 6 figure
- …