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Abstract-These days, many traditional end-user applications are said to "run fast enough" on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging.This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six worldclass machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in "clean C" so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.

Complex "fat operators" are important contributors to the efficiency of specialized hardware. This paper introduces two new techniques for constructing efficient fat operators featuring up to dozens of operations with arbitrary and irregular data and memory dependencies. These techniques focus on minimizing critical path length and loaduse delay, which are key concerns for irregular computations. Selective Depipelining(SDP) is a pipelining technique that allows fat operators containing several, possibly dependent, memory operations. SDP allows memory requests to operate at a faster clock rate than the datapath, saving power in the datapath and improving memory performance. Cachelets are small, customized, distributed L0 caches embedded in the datapath to reduce load-use latency. We apply these techniques to Conservation Cores(ccores) to produce coprocessors that accelerate irregular code regions while still providing superior energy efficiency. On average, these enhanced c-cores reduce EDP by 2× and area by 35% relative to c-cores. They are up to 2.5× faster than a general-purpose processor and reduce energy consumption by up to 8× for a variety of irregular applications including several SPECINT benchmarks. Recent work [27] examined one approach to improving the energy efficiency of these codes by converting dark silicon into a collection of energy-saving applicationspecialized cores called Conservation Cores, or c-cores. That approach emphasized energy savings while matching the performance of a conventional processor, but three factors limited its performance and energy efficiency gains. First, synchronization with the memory system restricted

Peer Instruction (PI) is an active learning pedagogical technique. PI lectures present students with a series of multiple-choice questions, which they respond to both individually and in groups. PI has been widely successful in the physical sciences and, recently, has been successfully adopted by computer science instructors in lower-division, introductory courses. In this work, we challenge readers to consider PI for their upper-division courses as well. We present a PI curriculum for two upper-division computer science courses: Computer Architecture and Theory of Computation. These courses exemplify several perceived challenges to the adoption of PI in upper-division courses, including: exploration of abstract ideas, development of high-level judgment of engineering design trade-offs, and exercising advanced mathematical sophistication. This work includes selected course materials illustrating how these challenges are overcome, learning gains results comparing these upper-division courses with previous lower-division results in the literature, student attitudinal survey results (N = 501), and pragmatic advice to prospective developers and adopters. We present three main findings. First, we find that these upper-division courses achieved student learning gains equivalent to those reported in successful lower-division computing courses. Second, we find that student feedback for each class was overwhelmingly positive, with 88% of students recommending PI for use in other computer science classes. Third, we find that instructors adopting the materials introduced here were able to replicate the outcomes of the instructors who developed the materials in terms of student learning gains and student feedback.

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Objective: To characterize the clinical presentation and outcomes of Kawasaki disease (KD) in infants <6 months of age as compared to those ≥6 months in Latin America. Methods: We evaluated 36 infants <6 months old and 940 infants ≥6 months old diagnosed with KD in Latin America. We compared differences in laboratory data, clinical presentation, treatment response, and coronary artery outcomes between the two cohorts. Results: The majority (78.1%) of infants and children ≥6 months of age were initially diagnosed with KD, as compared to only 38.2% of infants <6 months. Clinical features of KD were more commonly observed in the older cohort: oral changes (92 vs. Moreno et al. Kawasaki Disease Infants Latin America 75%, P = 0.0023), extremity changes (74.6 vs. 57.1%, P = 0.029), and cervical lymphadenopathy (67.6 vs. 37.1%, P = 0.0004). Whether treated in the first 10 days of illness or after the 10th day, infants <6 months were at greater risk of developing a coronary artery aneurysm compared to KD patients ≥6 months treated at the same point in the course of illness [≤10 days (53.8 vs. 9.4%, P = 0.00012); >10 days (50 vs. 7.4%, P = 0.043)]. Conclusion: Our data show that despite treatment in the first 10 days of illness, infants <6 months of age in Latin America have a higher risk of developing a coronary artery aneurysm. Delay in the diagnosis leads to larger coronary artery aneurysms disproportionately in these infants. Thus, suspicion for KD should be high in this vulnerable population.

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