Voltage Scaled Low Power DNN Accelerator Design on Reconfigurable Platform

The exponential emergence of Field-Programmable Gate Arrays (FPGAs) has accelerated research on hardware implementation of Deep Neural Networks (DNNs). Among all DNN processors, domain-specific architectures such as Google’s Tensor Processor Unit (TPU) have outperformed conventional GPUs (Graphics Processing Units) and CPUs (Central Processing Units). However, implementing low-power TPUs in reconfigurable hardware remains a challenge in this field. Voltage scaling, a popular approach for energy savings, can be challenging in FPGAs, as it may lead to timing failures if not implemented appropriately. This work presents an ultra-low-power FPGA implementation of a TPU for edge applications. We divide the systolic array of a TPU into different FPGA partitions based on the minimum slack value of different design paths of Multiplier Accumulators (MACs). Each partition uses different near-threshold (NTC) biasing voltages to run its FPGA cores. The biasing voltage for each partition is roughly calculated by the proposed static schemes. However, further calibration of biasing voltage is performed by the proposed runtime scheme. To overcome the timing failure caused by NTC, the MACs with higher minimum slack are placed in lower-voltage partitions, while the MACs with lower minimum slack paths are placed in higher-voltage partitions. The proposed architecture is implemented in a commercial platform, namely Vivado with Xilinx Artix-7 FPGA and academic platform VTR with 22 nm, 45 nm and 130 nm FPGAs. Any timing error caused by NTC can be caught by the Razor flipflop used in each MAC. The proposed voltage-scaled, partitioned systolic array can save 3.1% to 11.6% of dynamic power in Vivado and VTR tools, respectively, depending on the FPGA technology, partition size, number of partitions and biasing voltages. The normalized performance and accuracy of benchmark models running on our low-power TPU are very competitive compared to existing literature

Efficient Quantum Algorithm for SUBSET-SUM Problem

Problems in the complexity class $NP$ are not all known to be solvable, but are verifiable given the solution, in polynomial time by a classical computer. The complexity class $BQP$ includes all problems solvable in polynomial time by a quantum computer. Prime factorization is in $NP$ class, and is also in $BQP$ class, owing to Shor\u27s algorithm. The hardest of all problems within the $NP$ class are called $NP$-complete. If a quantum algorithm can solve an $NP$-complete problem in polynomial time, it would imply that a quantum computer can solve all problems in $NP$ in polynomial time. Here, we present a polynomial-time quantum algorithm to solve an $NP$-complete variant of the $SUBSET-SUM$ problem, thereby, rendering $NP\subseteq BQP$. We illustrate that given a set of integers, which may be positive or negative, a quantum computer can decide in polynomial time whether there exists any subset that sums to zero. There are many real-world applications of our result, such as finding patterns efficiently in stock-market data, or in recordings of the weather or brain activity. As an example, the decision problem of matching two images in image processing is $NP$-complete, and can be solved in polynomial time, when amplitude amplification is not required

Tectono-metamorphic transitions in the higher Himalayan sequence: A clue for Main Central Thrust (MCT) localization in Darjeeling-Sikkim Himalaya

International audienceThis study recognizes a transition in the modes of ductile deformation across the Himalayan Crystalline Complex (HCC) from field and microstructural evidence. This transition leads to the Main Central Thrust (MCT) zone localization in the Darjeeling-Sikkim Himalaya. In the initial stage of crustal shortening (prior to ∼16 Ma), the HCC experienced temporally varying distributed ductile strains that produced regionally occurring planar fabrics and folds of three generations. Thereafter, the tectonic transition (from distributed to localized deformation) at ∼16 Ma occurred, forming the MCT zone in the HCC. This MCT zone (∼4-6 km thick) marks a diffuse metamorphic/rheological boundary between the hot HCC and the relatively colder Lesser Himalayan terrane to the south. The field data suggests that the base of compressed reverse paleo-isograds at the bottom of the MCT zone coincides with the transition of quartz creep mechanisms, from sub-grain rotation (SGR) to grain boundary migration (GBM). The present study documents the prograde metamorphic imprints to establish the P-T conditions of these multiple deformation episodes. Finally, this study presents a conceptual tectonic model, based on previous laboratory findings, to propose a two-stage tectono-thermal evolution of the Higher Himalaya

Investigation on structural aspects of ZnO nano-crystal using radio-active ion beam and PAC

Nano-crystalline ZnO has been studied with perturbed angular correlation using 111m Cd, implanted at ISOLDE/CERN and X-ray diffraction using Rietveld analysis. The data show a gradual increase in the crystal size and stress for a sample annealed at 600 °C, and reaching nearly properties of standard ZnO with tempering at 1000 °C. The perturbed angular correlation data show a broad frequency distribution at low annealing temperatures and small particle sizes, whereas at high annealing temperature and larger crystal sizes, results similar to bulk ZnO have been obtained. The ZnO nano-crystalline samples were initially prepared through a wet chemical route, have been examined by Fourier Transform Infrared Spectroscopy (FT-IR) and chemical purity has been confirmed with Energy Dispersive X-ray (EDAX) analysis as well as Transmission Electron Microscopy (TEM)

Crystallization and preliminary X-ray diffraction studies of sortase A from Streptococcus pneumoniae

Sortases are cell-membrane-anchored cysteine transpeptidases that are essential for the assembly and anchoring of cell-surface adhesins in Gram-positive bacteria. Thus, they play critical roles in virulence, infection and colonization by pathogens. Sortases have been classified into four types based on their primary sequence and the target-protein motifs that they recognize. All Gram-positive bacteria express a class A housekeeping sortase (SrtA). Sortase A from Streptococcus pneumoniae (NP_358691) has been crystallized in two crystal forms. Diamond-shaped crystals of Delta N(59)SrtA diffracted to 4.0 angstrom resolution and belonged to a tetragonal system with unit-cell parameters a = b = 122.8, c = 86.5 angstrom, alpha = beta = gamma = 90 degrees, while rod-shaped crystals of Delta N(81)SrtA diffracted to 2.91 angstrom resolution and belonged to the monoclinic space group P2(1) with unit-cell parameters a = 66.8, b = 103.47, c = 74.79 angstrom, alpha = gamma = 90, beta = 115.65 degrees. The Matthews coefficient (V(M) = 2.77 angstrom(3) Da(-1)) with similar to 56% solvent content suggested the presence of four molecules in the asymmetric unit for Delta N(81)SrtA. Also, a multi-copy search using a monomer as a probe in the molecular-replacement method resulted in the successful location of four sortase molecules in the asymmetric unit, with statistics R = 41.61, R(free) = 46.44, correlation coefficient (CC) = 64.31, CC(free) = 57.67