Compact Fixed-Point Filter Implementation

The main difficulty in IIR-filter hardware implementation using fixed-point arithmetic is an accurate continuous to discrete model conversion. We propose an alternative approach to IIR-filters fixed-point implementation based on adaptive discrete operator selection (z-operator or $delta-$ operator) and filter parameters optimization. This approach provides significant reduction of utilized logic elements for the given level of implementation accuracy

A new generation 99 line Matlab code for compliance Topology Optimization and its extension to 3D

Compact and efficient Matlab implementations of compliance Topology Optimization (TO) for 2D and 3D continua are given, consisting of 99 and 125 lines respectively. On discretizations ranging from $3\cdot 10^{4}$ to $4.8\cdot10^{5}$ elements, the 2D version, named top99neo, shows speedups from 2.55 to 5.5 times compared to the well-known top88 code (Andreassen-etal 2011). The 3D version, named top3D125, is the most compact and efficient Matlab implementation for 3D TO to date, showing a speedup of 1.9 times compared to the code of Amir-etal 2014, on a discretization with $2.2\cdot10^{5}$ elements. For both codes, improvements are due to much more efficient procedures for the assembly and implementation of filters and shortcuts in the design update step. The use of an acceleration strategy, yielding major cuts in the overall computational time, is also discussed, stressing its easy integration within the basic codes.Comment: 17 pages, 8 Figures, 4 Table

Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices

A recent trend in DNN development is to extend the reach of deep learning applications to platforms that are more resource and energy constrained, e.g., mobile devices. These endeavors aim to reduce the DNN model size and improve the hardware processing efficiency, and have resulted in DNNs that are much more compact in their structures and/or have high data sparsity. These compact or sparse models are different from the traditional large ones in that there is much more variation in their layer shapes and sizes, and often require specialized hardware to exploit sparsity for performance improvement. Thus, many DNN accelerators designed for large DNNs do not perform well on these models. In this work, we present Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs. To deal with the widely varying layer shapes and sizes, it introduces a highly flexible on-chip network, called hierarchical mesh, that can adapt to the different amounts of data reuse and bandwidth requirements of different data types, which improves the utilization of the computation resources. Furthermore, Eyeriss v2 can process sparse data directly in the compressed domain for both weights and activations, and therefore is able to improve both processing speed and energy efficiency with sparse models. Overall, with sparse MobileNet, Eyeriss v2 in a 65nm CMOS process achieves a throughput of 1470.6 inferences/sec and 2560.3 inferences/J at a batch size of 1, which is 12.6x faster and 2.5x more energy efficient than the original Eyeriss running MobileNet. We also present an analysis methodology called Eyexam that provides a systematic way of understanding the performance limits for DNN processors as a function of specific characteristics of the DNN model and accelerator design; it applies these characteristics as sequential steps to increasingly tighten the bound on the performance limits.Comment: accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems. This extended version on arXiv also includes Eyexam in the appendi

Compact Millimeter-Wave Bandpass Filters Using Quasi-Lumped Elements in 0.13-um (Bi)-CMOS Technology for 5G Wireless Systems

© 2019 IEEE.A design methodology for a compact millimeter-wave on-chip bandpass filter (BPF) is presented in this paper. Unlike the previously published works in the literature, the presented method is based on quasi-lumped elements, which consists of a resonator with enhanced self-coupling and metal-insulator-metal capacitors. Thus, this approach provides inherently compact designs comparing with the conventional distributed elements-based ones. To fully understand the insight of the approach, simplified LC-equivalent circuit models are developed. To further demonstrate the feasibility of using this approach in practice, the resonator and two compact BPFs are designed using the presented models. All three designs are fabricated in a standard 0.13- \mu \text{m} (Bi)-CMOS technology. The measured results show that the resonator can generate a notch at 47 GHz with the attenuation better than 28 dB due to the enhanced self-coupling. The chip size, excluding the pads, is only 0.096 \times 0.294 mm 2. In addition, using the resonator for BPF designs, the first BPF has one transmission zero at 58 GHz with a peak attenuation of 23 dB. The center frequency of this filter is 27 GHz with an insertion loss of 2.5 dB, while the return loss is better than 10 dB from 26 to 31 GHz. The second BPF has two transmission zeros, and a minimum insertion loss of 3.5 dB is found at 29 GHz, while the return loss is better than 10 dB from 26 GHz to 34 GHz. Also, more than 20-dB stopband attenuation is achieved from dc to 20.5 GHz and from 48 to 67 GHz. The chip sizes of these two BPFs, excluding the pads, are only 0.076\times 0.296 mm 2 and 0.096\times 0.296 mm 2, respectively.Peer reviewe

An Electrically Programmable Split-Electrode Charge-Coupled Transversal Filter (EPSEF)

A CCD split-electrode transversal filter (EPSEF) with analog controlled tap weights is described. The programmable tap weighting utilizes a novel analog multiplier for sampled data, based on charge profiling underneath a resistive gate structure. The EPSEF device concept and the performance data of a prototype filter with eight programmable taps are presented. Applications of the EPSEF in several programmed filter functions and in an adaptive filter system are demonstrated

Toward Early-Warning Detection of Gravitational Waves from Compact Binary Coalescence

Rapid detection of compact binary coalescence (CBC) with a network of advanced gravitational-wave detectors will offer a unique opportunity for multi-messenger astronomy. Prompt detection alerts for the astronomical community might make it possible to observe the onset of electromagnetic emission from (CBC). We demonstrate a computationally practical filtering strategy that could produce early-warning triggers before gravitational radiation from the final merger has arrived at the detectors.Comment: 16 pages, 7 figures, published in ApJ. Reformatted preprint with emulateap

The PyCBC search for gravitational waves from compact binary coalescence

We describe the PyCBC search for gravitational waves from compact-object binary coalescences in advanced gravitational-wave detector data. The search was used in the first Advanced LIGO observing run and unambiguously identified two black hole binary mergers, GW150914 and GW151226. At its core, the PyCBC search performs a matched-filter search for binary merger signals using a bank of gravitational-wave template waveforms. We provide a complete description of the search pipeline including the steps used to mitigate the effects of noise transients in the data, identify candidate events and measure their statistical significance. The analysis is able to measure false-alarm rates as low as one per million years, required for confident detection of signals. Using data from initial LIGO's sixth science run, we show that the new analysis reduces the background noise in the search, giving a 30% increase in sensitive volume for binary neutron star systems over previous searches.Comment: 29 pages, 7 figures, accepted by Classical and Quantum Gravit