Hyperdimensional computing (HDC) is emerging as an efficient and robust computing paradigm that has strong resilience to various types of errors. The error robustness nature of HDC makes it a good match for error-prone memory systems. However, the mechanisms behind HDCs robustness are not fully understood. In this work, we propose HyperMetric, a framework to train highly robust and hardware-friendly HDC models. We found that HDC’s error resilience is driven by Hamming distance margin between hypervectors. Based on this, we propose HyperMetric training that is based on metric learning in order to optimize for high robustness. The experiments show that HyperMetric trained HDC models deliver up to 17W larger Hamming distance margin and up to 14.3 We accelerate HyperMetric trained models using ReRAM. As compared to state-of-the-art HDC algorithms OnlineHD and HyDREA, HyperMetric ReRAM accelerator is > 20% more accurate for computing-in-memory (CIM) errors and > 10% more accurate for bit errors even in the face of variations. Furthermore, HyperMetric hardware is 35% more accurate in comparison with existing tinyHD and GENERIC accelerators in the face of 3× ReRAM resistance variance, and 20% more accurate with BER of up to 20% due to voltage scaling while keeping a good balance between area, power, and processing laten

This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are known for their compactness and energy-efficiency. Given the hardware constraints and noise characteristics of the underlying RRAM crossbars, we propose online adaptation of pre-trained SNNs in real-time using Direct Feedback Alignment (DFA) against traditional backpropagation (BP). Direct Feedback Alignment (DFA) learning, that allows layer-parallel gradient computations, acts as a fast, energy & area-efficient method for online adaptation of SNNs on RRAM crossbars, unleashing better algorithmic performance against those adapted using BP. Through extensive simulations using our in-house hardware evaluation engine called DFA_Sim, we find that DFA achieves upto 64.1% lower energy consumption, 10.1% lower area overhead, and a 2.1× reduction in latency compared to BP, while delivering upto 7.55% higher inference accuracy on human activity recognition (HAR) tasks.

Hyperdimensional computing (HDC), a powerful paradigm for cognitive tasks, often demands hypervectors of high dimensions (e.g., 10,000) to achieve competitive accuracy. However, processing such large-dimensional data poses challenges for performance and energy efficiency, particularly on resource-constrained devices. In this paper, We present a framework to terminate bit-serial HDC inference early when sufficient confidence is attained in the prediction. This approach integrates a Naive Bayes model to replace the conventional associative memory in HDC. This transformation allows for a probabilistic interpretation of the model outputs, steering away from mere similarity measures. We reduce more than 70% of bits that need to be processed while maintaining comparable accuracy across diverse benchmarks. In addition, We show the adaptability of our early termination algorithm during on-the-fly learning scenarios.

Open Modification Search (OMS) is a promising algorithm for mass spectrometry analysis that enables the discovery of modified peptides. However, OMS encounters challenges as it exponentially extends the search scope. Existing OMS accelerators either have limited parallelism or struggle to scale effectively with growing data volumes. In this work, we introduce an OMS accelerator utilizing multi-level-cell (MLC) RRAM memory to enhance storage capacity by 3x. Through in-memory computing, we achieve up to 77x faster data processing with two to three orders of magnitude better energy efficiency. Testing was done on a fabricated MLC RRAM chip. We leverage hyperdimensional computing to tolerate up to 10% memory errors while delivering massive parallelism in hardware.

In this paper, we propose novel algorithms of beam-forming training for millimeter wave wireless communication systems. Instead of searching the whole codebook organized as a binary tree, we propose using Bayesian tree search algorithms to reduce the average delay of beamforming training. In addition, we design algorithms that derive an optimal threshold for the proposed opportunistic one-threshold tree search algorithm and an optimal pair of thresholds for the proposed opportunistic two-threshold tree search algorithm. Furthermore, we propose using quantization to efficiently calculate the likelihood ratio in the proposed opportunistic tree search algorithms. Our simulation results show that the proposed algorithms could significantly reduce the average delay of beamforming training.

Most previous work in household energy conservation has focused on rule-based home automation to achieve energy savings, with relatively few researchers focusing on context-aware technologies. As a result, user comfort is often disregarded and few solutions handle decision conflicts caused by multiple activities undertaken by multiple users. The main contribution of this work is twofold. First, a comprehensive human-centric and context-aware comfort index is proposed to evaluate how users feel under particular environmental conditions with regard to thermal, illumination, and appliance-usage preferences. Second, the energy savings is formulated into an optimization problem to minimize the total energy consumption, even under multiple user comfort constraints. Short-term evaluation in our simulated home environment resulted in energy savings of at least 28.98%. Long-term evaluation using a home simulator resulted in energy savings of 33.7%. Most importantly, the energy savings in both situations was achieved under multiple user comfort constraints, representing a truly human-centric living environment.

Arousal is essential in understanding human behavior and decision-making. In this work, we present a multimodal arousal rating framework that incorporates minimal set of vocal and non-verbal behavior descriptors. The rating framework and fusion techniques are unsupervised in nature to ensure that it can be readily-applicable and interpretable. Our proposed multimodal framework improves correlation to human judgment from 0.66 (vocal-only) to 0.68 (multimodal); analysis shows that the supervised fusion framework does not improve correlation. Lastly, an interesting empirical evidence demonstrates that the signal-based quantification of arousal achieves a higher agreement with each individual rater than the agreement among raters themselves. This further strengthens that machine-based rating is a viable way of measuring subjective humans' internal states through observing behavior features objectively.

We present our methods and results on participating in the Interspeech 2014 Computational Paralinguistics ChallengE (ComParE) of which the goal is to detect certain type of load of a speaker using acoustic features. There are in total seven classification models contributing to our final prediction, namely, neural network with rectified linear unit and dropout (ReLUNet), conditional restricted Boltzmann machine (CRBM), logistic regression (LR), support vector machine (SVM), Gaussian discriminant analysis (GDA), k-nearest neighbors (KNN), and random forest (RF). When linearly blending the predictions of these models, we are able to get significant improvements over the challenge baseline. Index Terms: Physical Load Detection, Cognitive Load Detection, Neural Network, Classification Models