Time series forecasting is shifting towards Edge AI, where models are trained and executed on edge devices instead of in the cloud. However, training forecasting models at the edge faces two challenges concurrently: (1) dealing with streaming data containing abundant noise, which can lead to degradation in model predictions, and (2) coping with limited on-device resources. Traditional approaches focus on simple statistical methods like ARIMA or neural networks, which are either not robust to sensor noise or not efficient for edge deployment, or both. In this paper, we propose a novel, robust, and lightweight method named KalmanHD for on-device time series forecasting using Hyperdimensional Computing (HDC). KalmanHD integrates Kalman Filter (KF) with HDC, resulting in a new regression method that combines the robustness of KF towards sensor noise and the efficiency of HDC. KalmanHD first encodes the past values into a high-dimensional vector representation, then applies the Expectation-Maximization (EM) approach as in KF to iteratively update the model based on the incoming samples. KalmanHD inherently considers the variability of each sample and thereby enhances robustness. We further accelerate KalmanHD by substituting the expensive matrix multiplication with efficient binary operations between the covariance and the encoded values. Our results show that KalmanHD achieves MAE comparable to the state-of-the-art noise-optimized NN-based methods while running $3.6-8.6\times$ faster on typical edge platforms. The source code is available at https://github.com/DarthIV02/Ka1manHD

On-device learning has emerged as a prevailing trend that avoids the slow response time and costly communication of cloud-based learning. The ability to learn continuously and indefinitely in a changing environment, and with resource constraints, is critical for real sensor deployments. However, existing designs are inadequate for practical scenarios with (i) streaming data input, (ii) lack of supervision and (iii) limited on-board resources. In this paper, we design and deploy the first on-device lifelong learning system called LifeHD for general IoT applications with limited supervision. LifeHD is designed based on a novel neurally-inspired and lightweight learning paradigm called Hyperdimensional Computing (HDC). We utilize a two-tier associative memory organization to intelligently store and manage high-dimensional, low-precision vectors, which represent the historical patterns as cluster centroids. We additionally propose two variants of LifeHD to cope with scarce labeled inputs and power constraints. We implement LifeHD on off-the-shelf edge platforms and perform extensive evaluations across three scenarios. Our measurements show that LifeHD improves the unsupervised clustering accuracy by up to 74.8% compared to the state-of-the-art NN-based unsupervised lifelong learning baselines with as much as 34.3x better energy efficiency. Our code is available at https://github.com/Orienfish/LifeHD.

Wildland fire modeling tools can ingest high resolution 3D vegetation models as inputs. However, data used to build the surface fuels in these models is often at a 30-meter resolution, which does not necessarily provide sufficient detail for accurate modeling of fires. Terrestrial laser scans are increasingly being used to collect detailed vegetation data that could be integrated with new approaches to fuel and fire modeling, but manual segmentation of scans is not scalable beyond a small number of scans. There is a need to automatically segment these high resolution point clouds as they are collected in the field, such that they may be leveraged by fuel and fire models for wildland fire response and mitigation and other applied climate science. This paper summarizes our early work on a labeling, visualization and machine learning pipeline for detailed segmentation of fuels. Specific contributions are: (1) a labeling approach involving 3 dimensional segmentation of point clouds using a point cloud processing engine; (2) a visualization approach using a computer graphics engine; and (3) early results from a deep learning modeling approach for fuel segmentation by category (live and dead) and size class (1, 10, 100 and 1000 hour fuels).

Increasing social acceptance of prescribed burns is an important element of ramping up these controlled burns to the scale required to effectively mitigate destructive wildfires through reduction of excessive fire fuel loads. As part of a Design Challenge, students created concept designs for physical or virtual installations that would increase public understanding and acceptance of prescribed burns as an important tool for ending devastating megafires. The proposals defined how the public would interact with the installation and the learning goals for participants. This poster provides an overview of the virtual reality (VR) pipeline created to develop working prototypes of the immersive experiences and VR games that were proposed by the finalists in the design challenge.