198 research outputs found
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design
Graph neural networks (GNNs) have shown significant accuracy improvements in
a variety of graph learning domains, sparking considerable research interest.
To translate these accuracy improvements into practical applications, it is
essential to develop high-performance and efficient hardware acceleration for
GNN models. However, designing GNN accelerators faces two fundamental
challenges: the high bandwidth requirement of GNN models and the diversity of
GNN models. Previous works have addressed the first challenge by using more
expensive memory interfaces to achieve higher bandwidth. For the second
challenge, existing works either support specific GNN models or have generic
designs with poor hardware utilization.
In this work, we tackle both challenges simultaneously. First, we identify a
new type of partition-level operator fusion, which we utilize to internally
reduce the high bandwidth requirement of GNNs. Next, we introduce
partition-level multi-threading to schedule the concurrent processing of graph
partitions, utilizing different hardware resources. To further reduce the extra
on-chip memory required by multi-threading, we propose fine-grained graph
partitioning to generate denser graph partitions. Importantly, these three
methods make no assumptions about the targeted GNN models, addressing the
challenge of model variety. We implement these methods in a framework called
SwitchBlade, consisting of a compiler, a graph partitioner, and a hardware
accelerator. Our evaluation demonstrates that SwitchBlade achieves an average
speedup of and energy savings of compared to the
NVIDIA V100 GPU. Additionally, SwitchBlade delivers performance comparable to
state-of-the-art specialized accelerators
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
A photo captured with bokeh effect often means objects in focus are sharp
while the out-of-focus areas are all blurred. DSLR can easily render this kind
of effect naturally. However, due to the limitation of sensors, smartphones
cannot capture images with depth-of-field effects directly. In this paper, we
propose a novel generator called Glass-Net, which generates bokeh images not
relying on complex hardware. Meanwhile, the GAN-based method and perceptual
loss are combined for rendering a realistic bokeh effect in the stage of
finetuning the model. Moreover, Instance Normalization(IN) is reimplemented in
our network, which ensures our tflite model with IN can be accelerated on
smartphone GPU. Experiments show that our method is able to render a
high-quality bokeh effect and process one pixel image in 1.9
seconds on all smartphone chipsets. This approach ranked First in AIM 2020
Rendering Realistic Bokeh Challenge Track 1 \& Track 2.Comment: accepted by ECCV workshop 202
Efficient Adaptive Activation Rounding for Post-Training Quantization
Post-training quantization attracts increasing attention due to its
convenience in deploying quantized neural networks. Although
rounding-to-nearest remains the prevailing method for DNN quantization, prior
research has demonstrated its suboptimal nature when applied to weight
quantization. They propose optimizing weight rounding schemes by leveraging
output error rather than the traditional weight quantization error. Our study
reveals that similar rounding challenges also extend to activation
quantization. Despite the easy generalization, the challenges lie in the
dynamic nature of activation. Adaptive rounding is expected for varying
activations and the method is subjected to runtime overhead. To tackle this, we
propose the AQuant quantization framework with a novel perspective to reduce
output error by adjusting rounding schemes of activations. Instead of using the
constant rounding border 0.5 of the rounding-to-nearest operation, we make the
border become a function w.r.t. the activation value to change the activation
rounding by the adaptive border. To deal with the runtime overhead, we use a
coarse-grained version of the border function. Finally, we introduce our
framework to optimize the border function. Extensive experiments show that
AQuant achieves notable improvements compared to state-of-the-art works and
pushes the accuracy of ResNet-18 up to 60.31% under the 2-bit weight and
activation quantization
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling
Weakly-supervised action localization aims to recognize and localize action
instancese in untrimmed videos with only video-level labels. Most existing
models rely on multiple instance learning(MIL), where the predictions of
unlabeled instances are supervised by classifying labeled bags. The MIL-based
methods are relatively well studied with cogent performance achieved on
classification but not on localization. Generally, they locate temporal regions
by the video-level classification but overlook the temporal variations of
feature semantics. To address this problem, we propose a novel attention-based
hierarchically-structured latent model to learn the temporal variations of
feature semantics. Specifically, our model entails two components, the first is
an unsupervised change-points detection module that detects change-points by
learning the latent representations of video features in a temporal hierarchy
based on their rates of change, and the second is an attention-based
classification model that selects the change-points of the foreground as the
boundaries. To evaluate the effectiveness of our model, we conduct extensive
experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The
experiments show that our method outperforms current state-of-the-art methods,
and even achieves comparable performance with fully-supervised methods.Comment: Accepted to ICCV 2023. arXiv admin note: text overlap with
arXiv:2203.15187, arXiv:2003.12424, arXiv:2104.02967 by other author
A novel low-temperature fabrication approach of composite phase change materials for high temperature thermal energy storage
Phase change materials (PCMs) are generally integrated into matrix materials to form shape-stabilized composite heat storage materials (HSMs) used for high temperature thermal energy storage applications. The conventional fabrication of composite HSMs is prevalently implemented at quite high temperatures, which is energy-intensive and narrows down the range of applicable PCMs because of thermal decomposition. Therefore, this paper establishes a novel fabrication approach to accomplish highly dense matrix to encapsulate PCMs at extremely low temperatures, based on the recently developed cold sintering process. The feasibility of the proposed approach was demonstrated by a case study of NaNO3/Ca(OH)2 composite HSMs. It was observed that the Ca(OH)2 matrix formed dense microstructure with obvious sintered boundaries and successfully encapsulated NaNO3 as PCM. The HSMs maintained stable macroscopic shape after hundreds of thermal cycles, and exhibited an energy storage efficiency of 59.48%, little leakage of PCM, and good thermal stability. Mechanical tests indicated that the HSMs possessed excellent mechanical properties when the sintering pressure is over 220 MPa. The discharging time of stored heat was presented through infrared thermography, and the heat storage capacity measured for the composite HSMs was over four times as high as those of typical solid storage materials of sensible heat, which demonstrated their excellent heat storage performances. The HSMs can be used in the form of packed bed or parallel channel with multi-layered heat storage, which is beneficial for efficiently utilizing solar heat and improving the performance of current energy storage system. This study therefore provides a novel route for energy-saving and low-carbon fabrication of shape-stabilized composite HSMs
Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training
An activation function is an element-wise mathematical function and plays a
crucial role in deep neural networks (DNN). Many novel and sophisticated
activation functions have been proposed to improve the DNN accuracy but also
consume massive memory in the training process with back-propagation. In this
study, we propose the nested forward automatic differentiation (Forward-AD),
specifically for the element-wise activation function for memory-efficient DNN
training. We deploy nested Forward-AD in two widely-used deep learning
frameworks, TensorFlow and PyTorch, which support the static and dynamic
computation graph, respectively. Our evaluation shows that nested Forward-AD
reduces the memory footprint by up to 1.97x than the baseline model and
outperforms the recomputation by 20% under the same memory reduction ratio.Comment: 8 pages, ICCD 202
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