Hyperdimensional Computing (HDC) has emerged as a lightweight learning paradigm garnering considerable attention in the IoT domain. Despite its appeal, HDC has lagged behind more intricate Machine Learning (ML) algorithms in accuracy, prompting prior research to propose sophisticated encoding and training techniques at the expense of efficiency. In this study, we present a novel approach for selecting projection vectors, used to encode input data into high-dimensional spaces, to enable HDC to attain high accuracy with significantly reduced vector sizes. We adopt a neural network-based mechanism to learn the projection vectors, and demonstrate their efficacy when integrated into a conventional HDC system. Furthermore, we introduce a novel sparsity technique to enhance hardware efficiency by compressing projection vectors and reducing computational operations with minimal impact on accuracy. Our experimental results reveal that at larger vector dimensions (e.g., 10k), our method (PIONEER), leveraging INT4 or binary vectors, outperforms the state-of-the-art high-precision nonlinear encoding in terms of accuracy, while preserving noteworthy accuracy even at extremely lower dimensions of 50–100. Additionally, by applying our proposed sparsification technique, PIONEER achieves significant performance and energy efficiency compared to previous work.

Contactless ECG monitoring with radar technology is used in both long-term and remote healthcare monitoring. While first attempts were able to only estimate heart rate, more recently deep learning (DL) has been used to infer the continuous ECG signal, which is essential for many health monitoring applications. However, the compute-intensive nature of DL models makes it hard to deploy and personalize them in low-power systems that are critical for remote healthcare monitoring. To address this challenge, we introduce HyperECG, a pioneering approach based on Hyperdimensional Computing (HDC), an efficient alternative machine learning method, to infer ECG signals from radar inputs. We combine a novel learnable HDC projection encoding with state-of-the-art HDC regressors to achieve high-quality ECG estimation. Experimental results reveal that HyperECG achieves output quality comparable to the state-of-the-art DL while reducing inference and training runtime up to$23 \times$and$36 \times$, respectively. HyperECG supports on-device model personalization, crucial in medical settings, with accuracy improvements of up to$68 \%$on patient-specific evaluations, compared to before fine-tuning HyperECG.

Hyperdimensional Computing (HDC), a promising alternative to address the limitations of edge devices, is not exempt from the security challenges confronted by machine learning algorithms, in particular, adversarial attacks. The limited body of research exploring the security implications of HDC overlooks its inherent algorithm. In this paper, we propose a novel and effective adversarial attack technique targeting HDC. Our approach analyzes and prioritizes the impact of input features as well as encoded elements on decision boundaries and perturbs the input towards incorrect decisions in a guided manner. We evaluate our method on different datasets and attack models (i.e., untargeted/targeted, white-box/gray-box). Experimental results indicate that our proposed design, HDXpose, significantly outperforms the state-of-the-art attack techniques by achieving higher success rate with smaller distortion and execution time, rendering its efficacy for real-time attack generation.

Hyperdimensional Computing (HDC) represents an emerging paradigm within the domain of cognitive computing, inspired by the information processing mechanisms observed in the human brain. Despite the research efforts devoted to improving and extending HDC algorithms and hardware, the efficacy and privacy of HDC remains challenging in handling image data. In this study, we highlight the accuracy, efficiency and privacy concerns of existing HDC-based methods on image data. We propose a novel vector-free encoding that shrinks the energy consumption and obviates the need for vector storage. We repurpose the released resources to augment the proposed encoding with a well crafted and privacy-aware feature extractor. Experimental results indicate that our proposed design, designated as VisionHD, gains a significant accuracy improvement (> 22%) while its energy consumption remains within the confines of the baseline HDC. To evaluate the privacy of VisionHD, we introduce a more effective and generic reversing technique, which reveals that VisionHD successfully obfuscates the information and improves the privacy metric by 16.9X.

As a lightweight and robust brain-inspired computing paradigm, Hyperdimensional Computing (HDC) serves as a promising solution for the next-generation edge AI. However, the basic form of HDC is vulnerable to privacy leaks and cyber attacks. In this paper, we breifly review and discuss the recent contributions to privacy and security of HDC. We first summarize existing HDC designs to protect against privacy leaks, such as differential privacy. Next, we review the data encryption techniques for collaborative learning using HDC based on Multi-Party Computation and Homomorphic Encryption. Finally, we discuss the HDC-based designs for combating cyber attacks in a malicious environment. More research on private and secure HDC-based methods are needed for future large-scale edge deployment.

In this paper, we propose TruLook, a framework that employs approximate computing techniques for GPU acceleration through computation reuse as well as approximate arithmetic operations to eliminate redundant and unnecessary exact computations. To enable computational reuse, GPU is enhanced with small lookup tables that are placed close to the stream cores that return already computed values for exact and potential inexact matches. Inexact matching is subject to a threshold controlled by the number of mantissa bits involved in the search. Approximate arithmetic is provided by a configurable approximate multiplier that dynamically detects and approximates operations which are not significantly affected by approximation. TruLook guarantees the accuracy bound required for an application by configuring the hardware at runtime. We have evaluated TruLook efficiency on a wide range of multimedia and deep learning applications. Our evaluation shows that with 0% and less than 1% quality loss budget, TruLook yields on average 2.1× and 5.6× energy-delay product improvement over four popular networks on the ImageNet dataset.

Hyperdimensional computing (HD) is an emerging paradigm for machine learning based on the evidence that the brain computes on high-dimensional, distributed, representations of data. The main operation of HD is encoding, which transfers the input data to hyperspace by mapping each input feature to a hypervector, followed by a bundling procedure that adds up the hypervectors to realize the encoding hypervector. The operations of HD are simple and highly parallelizable, but the large number of operations hampers the efficiency of HD in embedded domain. In this paper, we propose SHEARer, an algorithmhardware co-optimization to improve the performance and energy consumption of HD computing. We gain insight from a prudent scheme of approximating the hypervectors that, thanks to error resiliency of HD, has minimal impact on accuracy while provides high prospect for hardware optimization. Unlike previous works that generate the encoding hypervectors in full precision and then and then perform ex-post quantization, we compute the encoding hypervectors in an approximate manner that saves resources yet affords high accuracy. We also propose a novel FPGA architecture that achieves striking performance through massive parallelism with low power consumption. Moreover, we develop a software framework that enables training HD models by emulating the proposed approximate encodings. The FPGA implementation of SHEARer achieves an average throughput boost of 104,904× (15.7×) and energy savings of up to 56,044× (301×) compared to state-of-the-art encoding methods implemented on Raspberry Pi 3 (GeForce GTX 1080 Ti) using practical machine learning datasets.