Leveraging Signal Transfer Characteristics and Parasitics of Spintronic Circuits for Area and Energy-Optimized Hybrid Digital and Analog Arithmetic

While Internet of Things (IoT) sensors offer numerous benefits in diverse applications, they are limited by stringent constraints in energy, processing area and memory. These constraints are especially challenging within applications such as Compressive Sensing (CS) and Machine Learning (ML) via Deep Neural Networks (DNNs), which require dot product computations on large data sets. A solution to these challenges has been offered by the development of crossbar array architectures, enabled by recent advances in spintronic devices such as Magnetic Tunnel Junctions (MTJs). Crossbar arrays offer a compact, low-energy and in-memory approach to dot product computation in the analog domain by leveraging intrinsic signal-transfer characteristics of the embedded MTJ devices. The first phase of this dissertation research seeks to build on these benefits by optimizing resource allocation within spintronic crossbar arrays. A hardware approach to non-uniform CS is developed, which dynamically configures sampling rates by deriving necessary control signals using circuit parasitics. Next, an alternate approach to non-uniform CS based on adaptive quantization is developed, which reduces circuit area in addition to energy consumption. Adaptive quantization is then applied to DNNs by developing an architecture allowing for layer-wise quantization based on relative robustness levels. The second phase of this research focuses on extension of the analog computation paradigm by development of an operational amplifier-based arithmetic unit for generalized scalar operations. This approach allows for 95% area reduction in scalar multiplications, compared to the state-of-the-art digital alternative. Moreover, analog computation of enhanced activation functions allows for significant improvement in DNN accuracy, which can be harnessed through triple modular redundancy to yield 81.2% reduction in power at the cost of only 4% accuracy loss, compared to a larger network. Together these results substantiate promising approaches to several challenges facing the design of future IoT sensors within the targeted applications of CS and ML