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Emerging brain-inspired hyperdimensional computing (HDC) algorithms are vulnerable to timing and soft errors in associative memory used to store high-dimensional data representations. Such errors can significantly degrade HDC performance. A key challenge is error correction after an error in computation is detected. This work presents two novel error resilience frameworks for hyperdimensional computing systems. The first, called the checksum hypervector encoding (CHE) framework, relies on creation of a single additional hypervector that is a checksum of all the class hypervectors of the HDC system. For error resilience, elementwise validation of the checksum property is performed and those elements across all class vectors for which the property fails are removed from consideration. For an HDC system with K class hypervectors of dimension D, the second cross-hypervector clustering (CHC) framework clusters D, K-dimensional vectors consisting of the i-th element of each of the K HDC class hypervectors, 1 ≤ i ≤ K. Statistical properties of these vector clusters are checked prior to each hypervector query and all the elements of all K-dimensional vectors corresponding to statistical outlier vectors are removed as before. The choice of which framework to use is dictated by the complexity of the dataset to classify. Up to three orders of magnitude better resilience to errors than the state-of-the-art across multiple HDC high-dimensional encoding (representation) systems is demonstrated. 

Efficient and low-energy camera signal processing is critical for battery-supported sensing and surveillance applications. In this research, we develop a video object detection and tracking framework which adaptively down-samples frame pixels to minimize computation and memory costs, and thereby the energy consumed, while maintaining a high level of accuracy. Instead of always operating with the highest sensor pixel resolution (compute-intensive), video frame (pixel) content is down-sampled spatially, to adapt to changing camera environments (size of object tracked, peak-signal-tonoise- ratio (i.e, PSNR) of video frames). Object detection and tracking is supported by a novel video resolution-aware adaptive hyperdimensional computing framework. This leverages a low memory overhead non-linear hypervector encoding scheme specifically tailored for handling multiple degrees of resolution. Previous classification decisions of a moving object based on its tracking label are used to improve tracking robustness. Energy savings of up to 1.6 orders of magnitude and up to an order of magnitude compute speedup is obtained on a range of experiments performed on benchmark systems.