Federated learning is a distributed learning method by training the model in locally multiple clients, which has been used in numerous fields. Current convolutional neural networks (CNN)-based federated learning approaches face challenges from computational cost, communication efficiency, and robust communication. Recently, Hyper Dimensional Computing (HDC) has been recognized as a promising technique to address these challenges. HDC encodes data as high-dimensional vectors and enables lightweight training and communication through simple parallel vector operations. Several HDC-based federated learning methods have been proposed. Although existing methods reduce computational efficiency and communication cost, they are difficult to handle complex learning tasks and are not robust to unreliable wireless channels. In this work, we innovatively introduce a synergetic federated learning framework, FHDnn. With advantage of the complementary strengths of CNN and HDC, FHDnn can achieve optimal performance on complex image tasks while maintaining good computational and communication efficiency. Secondly, we demonstrate in detail the convergence of using HDC in a generalized federated learning framework, providing theoretical guarantees for HDC-based federated learning approach. Finally, we design three communication strategies to further improve the communication efficiency of FHDnn by 32×. Experiments demonstrate that FHDnn converges 3× faster than CNN-based federated learning methods, reduces the communication cost by 2,112×, and the local computation and energy consumption by 192×. In addition, it has good robustness to unreliable communication with bit errors, noise, and packet loss.

To answer the ever-increasing demand for high accuracy in artificial intelligence (AI)-based applications, several models have been proposed. Among them, ensemble learning, a technique that trains multiple classifiers and then combines their prediction during the inference stage, emerged as a promising approach. Despite being largely explored in the context of models like random forests or convolutional neural networks, very few research works have focused on ensemble learning targeting hyperdimensional computing (HDC). HDC is a brain-inspired computing paradigm that has gained momentum in the last decade because its lightweight and highly parallel operations make it an excellent alternative to compute-intense deep learning models for edge AI applications. In this work, we propose BagHD and BoostHD, two ensemble-based HDC implementations constructed using bagging and boosting, respectively. Accuracy evaluations indicate that our proposal improves baseline single-instance implementations and state-of-the-art HDC ensembles by up to 14% and 4%, respectively. We then leverage two key characteristics of HDC and ensemble learning to demonstrate how we can transform the proposed ensembles into equivalent single-instance implemen-tations, thus avoiding any memory and computing overhead during inference. In fact, when compared to traditional ordinary ensembles, we reduce memory requirements by up to 40x, improving accuracy at the same time. We also support ensemble learning HDC training in BagHD and BoostHD, showing that with little memory overhead it is possible to retrieve the original weak learners from the generated single-instance design.

The advent of IoT and advances in edge computing inspired federated learning, a distributed algorithm to enable on device learning. Transmission costs, unreliable networks and limited compute power all of which are typical characteristics of IoT networks pose a severe bottleneck for federated learning. In this work we propose FHDnn, a synergetic federated learning framework that combines the salient aspects of CNNs and Hyperdimensional Computing. FHDnn performs hyperdimensional learning on features extracted from a self-supervised contrastive learning framework to accelerate training, lower communication costs, and increase robustness to network errors by avoiding the transmission of the CNN and training only the hyperdimensional component. Compared to CNNs, we show through experiments that FHDnn reduces communication costs by 66X, local client compute and energy consumption by 1.5 - 6X, while being highly robust to network errors with minimal loss in accuracy.

Brain-inspired Hyperdimensional (HD) computing is a new machine learning approach that leverages simple and highly parallelizable operations. Unfortunately, none of the published HD computing algorithms to date have been able to accurately classify more complex image datasets, such as CIFAR100. In this work, we propose HDnn-PIM, that implements both feature extraction and HD-based classification for complex images by using processing-in-memory. We compare HDnn-PIM with HD-only and CNN implementations for various image datasets. HDnn-PIM achieves 52.4% higher accuracy as compared to pure HD computing. It also gains 1.2% accuracy improvement over state-of-the-art CNNs, but with 3.63x smaller memory footprint and 1.53x less MAC operations. Furthermore, HDnn-PIM is 3.6x-223x faster than RTX 3090 GPU, and 3.7x more energy efficient than state-of-the-art FloatPIM.

Homes are becoming more intelligent due to the growth of smart sensors and devices found in typical homes. However, most of these sensors and devices function independently from one another, limiting the amount of utility and services a truly "smart" home can provide. In this demonstration, we introduce two key ideas towards more intelligent homes. First, we explore the usage of mobile drones in the home environment. Second, we propose DIA, a system that seamlessly connects to the home environment and automatically discovers and jointly utilizes smart sensors and actuators around the home to provide services that are otherwise not possible. We demonstrate three services that DIA enables.

We propose a system that tracks each occupant’s personal share of energy use, or “energy footprint,” inside commercial building environments and provides insights to occupants on the real-time energy impact of their actions. We propose a new space-centric policy for fair apportionment of energy in shared environments and demonstrate a method for automatically determining space-centric energy zones. In this work, we design and implement ePrints, a system for tracking personalized energy usage in real-time. ePrints supports different apportionment policies, with microsecond-level footprint computation time and graceful scaling with size of building, frequency of energy updates, and rate of occupant location changes. Finally, we present applications enabled by our system, such as mobile and wearable applications to provide users timely feedback on the energy impacts of their actions, as well as applications to provide energy saving suggestions and inform building-level policies.

With smart buildings becoming popular, it is important to track the wastage of energy in public shared buildings to save energy. Current monitoring systems do not provide real-time visibility into the impact of occupants' actions on energy consumption of a building. We propose a system that tracks the energy consumed by users in shared spaces such as offices thereby making them aware and accountable for the energy they consume. Our system combines energy monitoring with localization techniques to generate real-time energy footprints for every occupant in a shared space and provides actionable feedback to them in the form of visualization.

With the prevalence of smartphones, pedestrians and joggers today often walk or run while listening to music. Since they are deprived of their auditory senses that would have provided important cues to dangers, they are at a much greater risk of being hit by cars or other vehicles. In this demonstration we present SEUS, a wearable system aimed at Sense Enhancement for Urban Safety. SEUS uses a three-stage architecture, consisting of headset mounted audio sensors, an embedded front-end for signal processing and feature extraction, and machine learning based classification on a smartphone, to provide early danger detection for pedestrians in real-time.