32 research outputs found
Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles
The recent advancements in wireless technology enable connected autonomous
vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such
as processed LIDAR and camera data from other vehicles. In this work, we design
an integrated information sharing and safe multi-agent reinforcement learning
(MARL) framework for CAVs, to take advantage of the extra information when
making decisions to improve traffic efficiency and safety. We first use weight
pruned convolutional neural networks (CNN) to process the raw image and point
cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data
with neighboring CAVs. We then design a safe actor-critic algorithm that
utilizes both a vehicle's local observation and the information received via
V2V communication to explore an efficient behavior planning policy with safety
guarantees. Using the CARLA simulator for experiments, we show that our
approach improves the CAV system's efficiency in terms of average velocity and
comfort under different CAV ratios and different traffic densities. We also
show that our approach avoids the execution of unsafe actions and always
maintains a safe distance from other vehicles. We construct an
obstacle-at-corner scenario to show that the shared vision can help CAVs to
observe obstacles earlier and take action to avoid traffic jams.Comment: This paper gets the Best Paper Award in the DCAA workshop of AAAI
202
EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System
IoT devices are increasingly being implemented with neural network models to
enable smart applications. Energy harvesting (EH) technology that harvests
energy from ambient environment is a promising alternative to batteries for
powering those devices due to the low maintenance cost and wide availability of
the energy sources. However, the power provided by the energy harvester is low
and has an intrinsic drawback of instability since it varies with the ambient
environment. This paper proposes EVE, an automated machine learning (autoML)
co-exploration framework to search for desired multi-models with shared weights
for energy harvesting IoT devices. Those shared models incur significantly
reduced memory footprint with different levels of model sparsity, latency, and
accuracy to adapt to the environmental changes. An efficient on-device
implementation architecture is further developed to efficiently execute each
model on device. A run-time model extraction algorithm is proposed that
retrieves individual model with negligible overhead when a specific model mode
is triggered. Experimental results show that the neural networks models
generated by EVE is on average 2.5X times faster than the baseline models
without pruning and shared weights
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning
Network pruning is a widely used technique to reduce computation cost and
model size for deep neural networks. However, the typical three-stage pipeline
significantly increases the overall training time. In this paper, we develop a
systematic weight-pruning optimization approach based on Surrogate Lagrangian
relaxation, which is tailored to overcome difficulties caused by the discrete
nature of the weight-pruning problem. We prove that our method ensures fast
convergence of the model compression problem, and the convergence of the SLR is
accelerated by using quadratic penalties. Model parameters obtained by SLR
during the training phase are much closer to their optimal values as compared
to those obtained by other state-of-the-art methods. We evaluate our method on
image classification tasks using CIFAR-10 and ImageNet with state-of-the-art
MLP-Mixer, Swin Transformer, and VGG-16, ResNet-18, ResNet-50 and ResNet-110,
MobileNetV2. We also evaluate object detection and segmentation tasks on COCO,
KITTI benchmark, and TuSimple lane detection dataset using a variety of models.
Experimental results demonstrate that our SLR-based weight-pruning optimization
approach achieves a higher compression rate than state-of-the-art methods under
the same accuracy requirement and also can achieve higher accuracy under the
same compression rate requirement. Under classification tasks, our SLR approach
converges to the desired accuracy faster on both of the datasets.
Under object detection and segmentation tasks, SLR also converges
faster to the desired accuracy. Further, our SLR achieves high model accuracy
even at the hard-pruning stage without retraining, which reduces the
traditional three-stage pruning into a two-stage process. Given a limited
budget of retraining epochs, our approach quickly recovers the model's
accuracy.Comment: arXiv admin note: text overlap with arXiv:2012.1007
PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment
Two-party computation (2PC) is promising to enable privacy-preserving deep
learning (DL). However, the 2PC-based privacy-preserving DL implementation
comes with high comparison protocol overhead from the non-linear operators.
This work presents PASNet, a novel systematic framework that enables low
latency, high energy efficiency & accuracy, and security-guaranteed 2PC-DL by
integrating the hardware latency of the cryptographic building block into the
neural architecture search loss function. We develop a cryptographic hardware
scheduler and the corresponding performance model for Field Programmable Gate
Arrays (FPGA) as a case study. The experimental results demonstrate that our
light-weighted model PASNet-A and heavily-weighted model PASNet-B achieve 63 ms
and 228 ms latency on private inference on ImageNet, which are 147 and 40 times
faster than the SOTA CryptGPU system, and achieve 70.54% & 78.79% accuracy and
more than 1000 times higher energy efficiency.Comment: DAC 2023 accepeted publication, short version was published on AAAI
2023 workshop on DL-Hardware Co-Design for AI Acceleration: RRNet: Towards
ReLU-Reduced Neural Network for Two-party Computation Based Private Inferenc