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  1. In this paper, we propose a new dynamic reliability technique using an accuracy-reconfigurable stochastic computing (ARSC) framework for deep learning computing. Unlike the conventional stochastic computing that conducts design time accuracy power/energy trade-off, the new ARSC design can adjust the bit-width of the data in run time. Hence, the ARSC can mitigate the long-term aging effects by slowing the system clock frequency, while maintaining the inference throughput by reducing the data bit-width at a small cost of accuracy. We show how to implement the recently proposed counter-based SC multiplication and bit-width reduction on a layer-wise quantization scheme for CNN networksmore »with dynamic fixed-point data. We validate an ARSC-based five-layer convolutional neural network designs for the MNIST dataset based on Vivado HLS with constraints from Xilinx Zynq-7000 family xc7z045 platform. Experimental results show that new ARSC DNN can sufficiently compensate the NBTI induced aging effects in 10 years with marginal classification accuracy loss while maintaining or even exceeding the pre-aging computing throughput. At the same time, the proposed ARSC computing framework also reduces the active power consumption due to the frequency scaling, which can further improve system reliability due to the reduced temperature.« less
  2. In this paper, we propose a novel accuracy-reconfigurable stochastic computing (ARSC) framework for dynamic reliability and power management. Different than the existing stochastic computing works, where the accuracy versus power/energy trade-off is carried out in the design time, the new ARSC design can change accuracy or bit-width of the data in the run-time so that it can accommodate the long-term aging effects by slowing the system clock frequency at the cost of accuracy while maintaining the throughput of the computing. We validate the ARSC concept on a discrete cosine transformation (DCT) and inverse DCT designs for image compressing/decompressing applications, whichmore »are implemented on Xilinx Spartan-6 family XC6SLX45 platform. Experimental results show that the new design can easily mitigate the long-term aging-induced effects by accuracy trade-off while maintaining the throughput of the whole computing process using simple frequency scaling. We further show that one-bit precision loss for the input data, which translated to 3.44dB of the accuracy loss in term of Peak Signal to Noise Ratio (PSNR) for images, we can sufficiently compensate the NBTI induced aging effects in 10 years while maintaining the pre-aging computing throughput of 7.19 frames per second. At the same time, we can save 74\% power consumption by 10.67dB of accuracy loss. The proposed ARSC computing framework also allows much aggressive frequency scaling, which can lead to order of magnitude power savings compared to the traditional dynamic voltage and frequency scaling (DVFS) techniques.« less
  3. The core mantle boundary (CMB), where the solid silicate mantle meets the liquid iron-nickel outer core, represents the largest density contrast on our planet, and it has long been recognized that the CMB is associated with significant structural heterogeneities. One CMB structure of particular interest are ultra low-velocity zones (ULVZs), laterally-varying, 5-50 km thick isolated patches seen in some locations just above the CMB that are associated with increased density and reduced seismic wave velocities. These variable characteristics have led to many questions regarding ULVZ origins, but less than 20% of the CMB has been surveyed for the presence ofmore »ULVZs given limited seismic coverage of the lowermost mantle. Therefore, investigations that sample the CMB with new geometries are critical to further our understanding of ULVZs and their potential connection to other deep Earth processes. The Transantarctic Mountains Northern Network (TAMNNET), a 15-station seismic array that was recently deployed in Antarctica, provides a unique dataset to further study ULVZ structure with new and unique path geometry. Core-reflected ScP phases from the TAMNNET dataset well sample the CMB in the vicinity of New Zealand in the southwestern Pacific, providing coverage between an area to the north where ULVZ structure has been previously identified and another region to the south, which shows no ULVZ evidence. This area is of particular interest because the data points sample across the boundary of the Pacific large low shear velocity province (LLSVP). The Weddell Sea region in Antarctica is also well sampled, providing new information on this area that has not been previously studied. By identifying and modeling pre- and post-cursor ScP energy, we have explored new portions of the CMB and found evidence for ULVZs in both regions. Given that ULVZs are detected within, along the edge of, and far from the Pacific LLSVP, our results may support that ULVZs are actually present everywhere along the CMB but that they are sometimes undetectable given associated methodology resolution.« less
  4. Abstract The Surface Enhancement of the IceTop air-shower array will include the addition of radio antennas and scintillator panels, co-located with the existing ice-Cherenkov tanks and covering an area of about 1 km 2 . Together, these will increase the sensitivity of the IceCube Neutrino Observatory to the electromagnetic and muonic components of cosmic-ray-induced air showers at the South Pole. The inclusion of the radio technique necessitates an expanded set of simulation and analysis tools to explore the radio-frequency emission from air showers in the 70 MHz to 350 MHz band. In this paper we describe the software modules thatmore »have been developed to work with time- and frequency-domain information within IceCube's existing software framework, IceTray, which is used by the entire IceCube collaboration. The software includes a method by which air-shower simulation, generated using CoREAS, can be reused via waveform interpolation, thus overcoming a significant computational hurdle in the field.« less
    Free, publicly-accessible full text available June 1, 2023