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Stream processing, which involves real-time computation of data as it is created or received, is vital for various applications, specifically wireless communication. The evolving protocols, the requirement for high-throughput, and the challenges of handling diverse processing patterns make it demanding. Traditional platforms grapple with meeting real-time throughput and latency requirements due to large data volume, sequential and indeterministic data arrival, and variable data rates, leading to inefficiencies in memory access and parallel processing. We present Canalis, a throughput-optimized framework designed to address these challenges, ensuring high-performance while achieving low energy consumption. Canalis is a hardware-software co-designed system. It includes a programmable spatial architecture, Flux Stream Processing Unit (FluxSPU), proposed by this work to enhance data throughput and energy efficiency. FluxSPU is accompanied by a software stack that eases the programming process. We evaluated Canalis with eight distinct benchmarks. When compared to CPU and GPU in mobile SoC to demonstrate the effectiveness of domain specialization, Canalis achieves an average speedup of 13.4\(\times\)and 6.6\(\times\), and energy savings of 189.8\(\times\)and 283.9\(\times\), respectively. In contrast to equivalent ASICs of the benchmarks, the average energy overhead of Canalis is within 2.4\(\times\), successfully maintaining generalizations without incurring significant overhead. 

Genomics is playing an important role in transforming healthcare. Genetic data, however, is being produced at a rate that far outpaces Moore's Law. Many efforts have been made to accelerate genomics kernels on modern commodity hardware, such as CPUs and GPUs, as well as custom accelerators (ASICs) for specific genomics kernels. While ASICs provide higher performance and energy efficiency than general-purpose hardware, they incur a high hardware-design cost. Moreover, to extract the best performance, ASICs tend to have significantly different architectures for different kernels. The divergence of ASIC designs makes it difficult to run commonly used modern sequencing analysis pipelines due to software integration and programming challenges. With the observation that many genomics kernels are dominated by dynamic programming (DP) algorithms, this paper presents GenDP, a framework of dynamic programming acceleration includingDPAx, a DP accelerator, andDPMap, a graph-partitioning algorithm that maps DP objective functions to the accelerator. DPAx supports DP kernels with various dependency patterns, such as 1D and 2D DP tables and long-range dependencies in the graph structure. DPAx also supports different DP objective functions and precisions required for genomics applications. GenDP is evaluated on genomics kernels in both short-read and long-read analysis pipelines, achieving 157.8 $\times$ $throughput/m{m}^2$over GPU baselines and 132.0 $\times$ $throughput/m{m}^2$over CPU baselines. 

Each fall, millions of monarch butterflies across the northern US and Canada migrate up to 4,000km to overwinter in specific mountain peaks in central Mexico. To track monarchs precisely and study their navigation, a monarch tracker must obtain daily localization of the butterfly as it progresses on its three-month journey. And, the tracker must perform this task while having a weight in the tens of milligrams (mg) and measuring a few millimeters (mm) in size to avoid interfering with the monarch's flight. This paper proposes mSAIL, 8 × 8 × 2.6mm and 62mg embedded system for monarch migration tracking, constructed using 8 prior custom-designed ICs providing solar energy harvesting, an ultra-low power processor, light/temperature sensors, power management, and a wireless transceiver, all integrated and 3D stacked on a micro PCB with an 8 × 8mm printed antenna. The proposed system is designed to record and compress light and temperature data during the migration path while harvesting solar energy for energy autonomy, and wirelessly transmit the data at the overwintering site in Mexico, from which the daily location of the butterfly can be estimated using a deep learning-based localization algorithm. A two-day trial experiment of mSAIL attached to a live butterfly in an outdoor botanical garden demonstrates the feasibility of individual butterfly localization and tracking. 

Abstract Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool—LAMPrey—to process sequenced LAMP product. LAMPrey’s concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.