Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented as high-dimensional random vectors. The technique has a rigorous mathematical backing, and is easy to implement in energy-efficient and highly parallel hardware like FPGAs and "processing-in-memory" architectures. The effectiveness of HDC in machine learning largely depends on how raw data is mapped to high-dimensional space. In this work, we propose NysHD, a new method for constructing this mapping that is based on the Nyström method from the literature on kernel approximation. Our approach provides a simple recipe to turn any user-defined positive-semidefinite similarity function into an equivalent mapping in HDC. There is a vast literature on the design of such functions for learning problems. Our approach provides a mechanism to import them into the HDC setting, expanding the types of problems that can be tackled using HDC. Empirical evaluation against existing HDC encoding methods shows that NysHD can achieve, on average, 11% and 17% better classification accuracy on graph and string datasets respectively.

Learning-based environmental sound recognition has emerged as a crucial method for ultra-low-power environmental monitoring in biological research and city-scale sensing systems. These systems usually operate under limited resources and are often powered by harvested energy in remote areas. Recent efforts in on-device sound recognition suffer from low accuracy due to resource constraints, whereas cloud offloading strategies are hindered by high communication costs. In this work, we introduce ORCA, a novel resource-efficient cloud-assisted environmental sound recognition system on batteryless devices operating over the Low-Power Wide-Area Networks (LPWANs), targeting wide-area audio sensing applications. We propose a cloud assistance strategy that remedies the low accuracy of on-device inference while minimizing the communication costs for cloud offloading. By leveraging a self-attention-based cloud sub-spectral feature selection method to facilitate efficient on-device inference, ORCA resolves three key challenges for resource-constrained cloud offloading over LPWANs: 1) high communication costs and low data rates, 2) dynamic wireless channel conditions, and 3) unreliable offloading. We implement ORCA on an energy-harvesting batteryless microcontroller and evaluate it in a real world urban sound testbed. Our results show that ORCA outperforms state-of-the-art methods by up to 80× in energy savings and 220× in latency reduction while maintaining comparable accuracy.

Federated Learning (FL) has gained increasing interest as a privacy-preserving distributed learning paradigm in recent years. Although previous works have addressed data and system heterogeneities in FL, there has been less exploration of modality heterogeneity, where clients collect data from various sensor types such as accelerometer, gyroscope, etc. As a result, traditional FL methods assuming uni-modal sensors are not applicable in multimodal federated learning (MFL). State-of-the-art MFL methods use modality-specific blocks, usually recurrent neural networks, to process each modality. However, executing these methods on edge devices proves challenging and resource-intensive. A new MFL algorithm is needed to jointly learn from heterogeneous sensor modalities while operating within limited resources and energy. We propose a novel hybrid framework based on Hyperdimensional Computing (HD) and deep learning, named MultimodalHD, to learn effectively and efficiently from edge devices with different sensor modalities. MultimodalHD uses a static HD encoder to encode raw sensory data from different modalities into high-dimensional low-precision hypervectors. These multimodal hypervectors are then fed to an attentive fusion module for learning richer representations via inter-modality attention. Moreover, we design a proximity-based aggregation strategy to alleviate modality interference between clients. MultimodalHD is designed to fully utilize the strengths of both worlds: the computing efficiency of HD and the capability of deep learning. We conduct experiments on multimodal human activity recognition datasets. Results show that MultimodalHD delivers comparable (if not better) accuracy compared to state-of-the-art MFL algorithms, while being 2x – 8x more efficient in terms of training time. Our code is available online1.

On-device environmental sound classification (ESC) in rural areas faces one major challenge of resource efficiency. Traditional methods rely on resource-intensive machine learning models, making them impractical for small edge devices like microcontrollers (MCUs). This poster presents SoundHD, a novel ESC solution using Hyperdimensional Computing (HDC), a brain-inspired and lightweight computing paradigm. We further optimize the memory footprint for deployment on MCUs. Our initial results show that SoundHD can be deployed and executed effectively on memory-constrained MCUs.

With the continuing advancement of ubiquitous computing and various sensor technologies, we are observing a massive population of multimodal sensors at the edge which posts significant challenges in fusing the data. In this poster we propose MultimodalHD, a novel Hyperdimensional Computing (HD)-based design for learning from multimodal data on edge devices. We use HD to encode raw sensory data to high-dimensional low-precision hypervectors, after which the multimodal hypervectors are fed to an attentive fusion module for learning richer representations via inter-modality attention. Our experiments on multimodal time-series datasets show MultimodalHD to be highly efficient. MultimodalHD achieves 17x and 14x speedup in training time per epoch on HAR and MHEALTH datasets when comparing with state-of-the-art RNNs, while maintaining comparable accuracy performance.

Federated Learning (FL) is a widely adopted distributed learning paradigm for to its privacy-preserving and collaborative nature. In FL, each client trains and sends a local model to the central cloud for aggregation. However, FL systems using neural network (NN) models are expensive to deploy on constrained edge devices regarding computation and communication. In this demo, we present FedHD, a FL system using Hyperdimensional Computing (HDC). In contrast to NN, HDC is a brain-inspired and lightweight computing paradigm using high-dimensional vectors and associative memory. Our measurements indicate that FedHD is 3.2×, 3.2×, 5× better on performance, energy and communication efficiency respectively compared to NN-based FL systems whilst maintaining similar accuracy to the state of the art. Our code is available on GitHub1.