Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference

• URL: http://arxiv.org/abs/2204.10539v1
• Date: Fri, 22 Apr 2022 07:07:38 GMT
• Title: Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference
• Authors: Chen Xie, Daniele Jahier Pagliari, Andrea Calimera
• Abstract summary: Low-resolution infrared (IR) sensors can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. We propose an energy-efficient adaptive inference solution consisting of a cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN) We show that, when processing the output of a 8x8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach.
• Score: 4.158182639870093
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Low-resolution infrared (IR) Sensors combined with machine learning (ML) can be leveraged to implement privacy-preserving social distance monitoring solutions in indoor spaces. However, the need of executing these applications on Internet of Things (IoT) edge nodes makes energy consumption critical. In this work, we propose an energy-efficient adaptive inference solution consisting of the cascade of a simple wake-up trigger and a 8-bit quantized Convolutional Neural Network (CNN), which is only invoked for difficult-to-classify frames. Deploying such adaptive system on a IoT Microcontroller, we show that, when processing the output of a 8x8 low-resolution IR sensor, we are able to reduce the energy consumption by 37-57% with respect to a static CNN-based approach, with an accuracy drop of less than 2% (83% balanced accuracy).

Related papers

• A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules [0.0]
  This paper presents a novel cloud-edge framework for addressing computational and energy constraints in complex control systems. By integrating a biologically plausible learning method with local plasticity rules, we harness the efficiency, scalability, and low latency of Spiking Neural Networks (SNNs) This design replicates control signals from a cloud-based controller directly on the plant, reducing the need for constant plant-cloud communication.
  arXiv  Detail & Related papers  (2024-04-12T22:34:17Z)
• HW-SW Optimization of DNNs for Privacy-preserving People Counting on Low-resolution Infrared Arrays [9.806742394395322]
  Low-resolution infrared (IR) array sensors enable people counting applications such as monitoring the occupancy of spaces and people flows. Deep Neural Networks (DNNs) have been shown to be well-suited to process these sensor data in an accurate and efficient manner. We propose a highly automated full-stack optimization flow for DNNs that goes from neural architecture search, mixed-precision quantization, and post-processing.
  arXiv  Detail & Related papers  (2024-02-02T08:45:38Z)
• Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline [5.8859061623552975]
  We present a smart vision sensor System on Chip (SoC), featuring an event-based camera and a low-power asynchronous spiking Convolutional Neural Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. We present the asynchronous architecture, the individual blocks, and the sCNN processing principle and benchmark against other sCNN capable processors.
  arXiv  Detail & Related papers  (2023-04-13T19:28:57Z)
• Fast Exploration of the Impact of Precision Reduction on Spiking Neural Networks [63.614519238823206]
  Spiking Neural Networks (SNNs) are a practical choice when the target hardware reaches the edge of computing. We employ an Interval Arithmetic (IA) model to develop an exploration methodology that takes advantage of the capability of such a model to propagate the approximation error.
  arXiv  Detail & Related papers  (2022-11-22T15:08:05Z)
• Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs [10.80166668204102]
  Low-resolution infrared (IR) array sensors offer a low-cost, low-power, and privacy-preserving alternative to optical cameras and smartphones/wearables. We demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN) We show that our best CNN achieves 86.3% balanced accuracy, significantly outperforming the 61% achieved by a state-of-the-art deterministic algorithm.
  arXiv  Detail & Related papers  (2022-04-22T07:17:45Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles [61.27933385742613]
  We propose a closed-loop learning flow for autonomous driving mini-vehicles that includes the target environment in-the-loop. We leverage a family of tinyCNNs to control the mini-vehicle, which learn in the target environment by imitating a computer vision algorithm, i.e., the expert. When running the family of CNNs, our solution outperforms any other implementation on the STM32L4 and k64f (Cortex-M4), reducing the latency by over 13x and the energy consummation by 92%.
  arXiv  Detail & Related papers  (2020-07-01T07:54:26Z)
• Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection [99.94079901071163]
  This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs) We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%.
  arXiv  Detail & Related papers  (2020-04-03T17:36:26Z)
• Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection [0.18459705687628122]
  We focus on the inference of neural networks running in microcontrollers and low-power processors. We adapted an existing convolutional-recurrent neural network to detect and classify cardiac arrhythmias. We show our implementation in fixed-point precision, using the CMSIS-NN libraries, with a memory footprint of 195.6KB, and a throughput of 33.98MOps/s.
  arXiv  Detail & Related papers  (2020-01-08T10:35:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.