Maximizing Memory-Level Parallelism via Integrated Stochastic Logic-in-Memory Architectures

Today's high-performance architectures are increasingly constrained by data movement latency and energy overhead, as the slowdown of single-core performance scaling coincides with the rise of highly data-intensive workloads. In-memory architectures have emerged as a complementary solution to conventional von Neumann systems by alleviating memory bandwidth bottlenecks, exploiting massive concurrency, and mitigating excessive data movement between memory and processing units. This study proposes a parallel in-memory stochastic computing (SC) architecture that implements an end-to-end computation pipeline within Magnetic Tunnel Junction (MTJ)-based memory augmented with logic-in-memory (LIM) capabilities. By leveraging the inherent stochasticity and write-read characteristics of MTJ devices, the proposed architecture enables a fully parallel and deterministic conversion of binary operands into probabilistic bit-streams, eliminating the need for energy-intensive external random number generation circuitry. These bit-streams are processed by parallel stochastic arithmetic units integrated directly within the memory arrays to efficiently implement core arithmetic and transcendental functions with minimal hardware complexity and inherent noise tolerance. The resulting stochastic outputs can be either reused as an input of future stochastic processing or converted back to binary form using parallel accumulation mechanisms and stored in the MTJ memory. By tightly integrating data storage, bit-stream generation, and computation within a unified in-memory fabric, the proposed design maximizes memory-level parallelism while substantially minimizing data movement.

AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection

Altitude sickness is a potentially life-threatening condition that impacts many individuals traveling to elevated altitudes. Timely detection is critical as symptoms can escalate rapidly. Early recognition enables simple interventions such as descent, oxygen, or medication, and prompt treatment can save lives by significantly lowering the risk of severe complications. Although conventional machine learning (ML) techniques have been applied to identify altitude sickness using physiological signals, such as heart rate, oxygen saturation, respiration rate, blood pressure, and body temperature, they often struggle to balance predictive performance with low hardware demands. In contrast, hyperdimensional computing (HDC) remains under-explored for this task with limited biomedical features, where it may offer a compelling alternative to existing classification models. Its vector symbolic framework is inherently suited to hardware-efficient design, making it a strong candidate for low-power systems like wearables. Leveraging lightweight computation and efficient streamlined memory usage, HDC enables real-time detection of altitude sickness from physiological parameters collected by wearable devices, achieving accuracy comparable to that of traditional ML models. We present AMS-HD, a novel system that integrates tailored feature extraction and Hadamard HV encoding to enhance both the precision and efficiency of HDC-based detection. This framework is well-positioned for deployment in wearable health monitoring platforms, enabling continuous, on-the-go tracking of acute altitude sickness.

TranSC: Hardware-Aware Design of Transcendental Functions Using Stochastic Logic

The hardware-friendly implementation of transcendental functions remains a longstanding challenge in design automation. These functions, which cannot be expressed as finite combinations of algebraic operations, pose significant complexity in digital circuit design. This study introduces a novel approach, TranSC, that utilizes stochastic computing (SC) for lightweight yet accurate implementation of transcendental functions. Building on established SC techniques, our method explores alternative random sources-specifically, quasi-random Van der Corput low-discrepancy (LD) sequences-instead of conventional pseudo-randomness. This shift enhances both the accuracy and efficiency of SC-based computations. We validate our approach through extensive experiments on various function types, including trigonometric, hyperbolic, and activation functions. The proposed design approach significantly reduces MSE by up to 98% compared to the state-of-the-art solutions while reducing hardware area, power consumption, and energy usage by 33%, 72%, and 64%, respectively.