A 15-Gbps BiCMOS XNOR gate for fast recognition of COVID-19 in binarized neural networks

The COVID-19 pandemic is spreading around the world causing more than 177 million cases and over 3.8 million deaths according to the European Centre for Disease Prevention and Control. The virus has devastating effects on economies, health, and well-being of worldwide population. Due to the high increase in daily cases, the available number of COVID-19 test kits in under-developed countries is scarce. Hence, it is vital to implement an effective screening method of patients using chest radiography since the equipment already exists. With the presence of automatic detection systems, any abnormalities in chest radiography that characterizes COVID-19 can be identified. Several artificial-intelligence algorithms have been proposed to detect the virus. However, neural networks training is considered to be time-consuming. Since computations in training neural networks are spent on floating-point multiplications, high computational power is required. Multipliers consume the most space and power among all arithmetic operators in deep neural networks. This paper proposes a 15 Gbps high-speed bipolar-complementary-metal-oxide-semiconductor (BiCMOS) exclusive-nor (XNOR) gate to replace multipliers in binarized neural networks. The proposed gate can be implemented on BiCMOS-based field-programmable gate arrays (FPGAs). This will significantly improve the response time in identifying chest abnormalities in CT scans and X-rays.

Performance Analysis of Ring Oscillators and Current-Starved VCO in 45-nm CMOS Technology

This paper presents a relative study among two Ring oscillators architecture (CMOS, NMOS) and current-starved Voltage-controlled oscillator (CS-VCO) on the basis of different parameters like power dissipation ,phase noise etc. All the design has been done in 45- nm CMOS technology node and 2.3 GHz Centre frequency have been taken for the comparison because of their applications in AV Devices and Radio control. An inherent idea of the given performance parameters has been realize by thecomparative study. The comparative data shows that NMOS based Ring oscillator is good option in terms of the phase noise performance. In this study NMOS Ring Oscillator have attain a phase noise -97.94 dBc/Hz at 1 MHz offset frequency from 2.3 GHz center frequency. The related data also shows that CMOS Ring oscillator is the best option in terms of power consumption. In this work CMOS Ring oscillator evacuatea power of 1.73 mW which is quite low. Keywords: Voltage controlled oscillator (VCO), phase noise, power consumption, Complementary metal-oxide-semiconductor (CMOS), Current Starved Voltage-Controlled Oscillator (CS- VCO), Pull up network (PUN), Pull down network (PDN)

Spin wave propagation in uniform waveguide: effects, modulation and its application

With the advent of the post-Moore era, researches on beyond-Complementary Metal Oxide Semiconductor (CMOS) approaches have been attracted more and more attention. Magnonics, or spin wave is one of the most promising technology beyond CMOS, which magnons-quanta for spin waves-process the information analogous to electronic charges in electronics. Information transmission by spin waves, which uses the frequency, amplitude and (or) phase to encode information, has a great many of advantages such as extremely low energy loss and wide-band frequency. Moreover, using the nonlinear characteristics of spin waves for information transmission can increase the extra degree of freedom of information. This review provides a tutorial overview over the effects of spin wave propagation and recent research progress in uniform spin wave waveguide. The propagation characteristics of spin waves in uniform waveguides and some special propagation phenomena such as spin wave beam splitting and self-focusing are described by combining experimental phenomena and theoretical formulas. Furthermore, we summarize methods for modulating propagation of spin wave in uniform waveguide, and comment on the advantages and limitations of these methods. The review may promote the development of information transmission technology based on spin waves.

Multipurpose Bio-Monitored Integrated Circuit in a Contact Lens Eye-Tracker

We present the design, fabrication, and test of a multipurpose integrated circuit (Application Specific Integrated Circuit) in AMS 0.35 µm Complementary Metal Oxide Semiconductor technology. This circuit is embedded in a scleral contact lens, combined with photodiodes enabling the gaze direction detection when illuminated and wirelessly powered by an eyewear. The gaze direction is determined by means of a centroid computation from the measured photocurrents. The ASIC is used simultaneously to detect specific eye blinking sequences to validate target designations, for instance. Experimental measurements and validation are performed on a scleral contact lens prototype integrating four infrared photodiodes, mounted on a mock-up eyeball, and combined with an artificial eyelid. The eye-tracker has an accuracy of 0.2°, i.e., 2.5 times better than current mobile video-based eye-trackers, and is robust with respect to process variations, operating time, and supply voltage. Variations of the computed gaze direction transmitted to the eyewear, when the eyelid moves, are detected and can be interpreted as commands based on blink duration or using blinks alternation on both eyes.

In-sensor optoelectronic computing using electrostatically doped silicon

Complementary metal-oxide-semiconductor (CMOS) image sensors are a visual outpost of many machines that interact with the world. While they presently separate image capture in front-end silicon photodiode arrays from image processing in digital back-ends, efforts to process images within the photodiode array itself are rapidly emerging, in hopes of minimizing the data transfer between sensing and computing, and the associated overhead in energy and bandwidth. Electrical modulation, or programming, of photocurrents is requisite for such in-sensor computing, which was indeed demonstrated with electrostatically doped, but non-silicon, photodiodes. CMOS image sensors are currently incapable of in-sensor computing, as their chemically doped photodiodes cannot produce electrically tunable photocurrents. Here we report in-sensor computing with an array of electrostatically doped silicon p-i-n photodiodes, which is amenable to seamless integration with the rest of the CMOS image sensor electronics. This silicon-based approach could more rapidly bring in-sensor computing to the real world due to its compatibility with the mainstream CMOS electronics industry. Our wafer-scale production of thousands of silicon photodiodes using standard fabrication emphasizes this compatibility. We then demonstrate in-sensor processing of optical images using a variety of convolutional filters electrically programmed into a 3 × 3 network of these photodiodes.

A Monolithic Stochastic Computing Architecture for Energy and Area Efficient Arithmetic

As the energy and hardware investments necessary for conventional high-precision digital computing continues to explode in the emerging era of artificial intelligence, deep learning, and Big-data [1-4], a change in paradigm that can trade precision for energy and resource efficiency is being sought for many computing applications. Stochastic computing (SC) is an attractive alternative since unlike digital computers, which require many logic gates and a high transistor volume to perform basic arithmetic operations such as addition, subtraction, multiplication, sorting etc., SC can implement the same using simple logic gates [5, 6]. While it is possible to accelerate SC using traditional silicon complementary metal oxide semiconductor (CMOS) [7, 8] technology, the need for extensive hardware investment to generate stochastic bits (s-bit), the fundamental computing primitive for SC, makes it less attractive. Memristor [9-11] and spin-based devices [12-15] offer natural randomness but depend on hybrid designs involving CMOS peripherals for accelerating SC, which increases area and energy burden. Here we overcome the limitations of existing and emerging technologies and experimentally demonstrate a standalone SC architecture embedded in memory based on two-dimensional (2D) memtransistors. Our monolithic and non-von Neumann SC architecture consumes a miniscule amount of energy < 1 nano Joules for s-bit generation and to perform arithmetic operations and occupy small hardware footprint highlighting the benefits of SC.

Design of a Fully Integrated VHF CP-PLL Frequency Synthesizer with an All-Digital Defect-Oriented Built-In Self-Test.

This paper presents the design of an on-chip charge pump phase-locked loop (CP-PLL) with a fully digital defect oriented built-in self-test (BIST) for very-high frequency (VHF) applications. The frequency synthesizer has a 40 to 100 MHz tuning range and uses a ring voltage-controlled oscillator for frequency synthesis. The PLL exhibits a phase noise of -132 dBc/Hz at 1 MHz and consumes 1.8 mW on a 3 V supply. The BIST implementation uses fewer external input or output, is capable of efficient fault diagnosis, and is compact, posing a low area overhead. The integrated circuit design was realized in the AMI 0.6µ complementary metal oxide-semiconductor process.

Ferroelectric-based synapses and neurons for neuromorphic computing

The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

Efficient techniques of fractional-N PLL for pervasive wireless applications

Purpose The purpose of this paper to ensure the rapid developments in the radio frequency wireless technology, the synthesis of frequencies for pervasive wireless applications is crucial by implementing the design of low voltage and low power Fractional-N phase locked loop (PLL) for controlling medical devices to monitor remotely patients. Design/methodology/approach The developments urge a technique reliable to phase noise in designing fractional-N PLL with a new eight transistor phase frequency detector and a good linearized charge pump (CP) for speed of operation with minimum mismatches. Findings In applications for portable wireless devices, by proposing a new phase-frequency detector with the removal of dead, blind zones and a modified CP to minimize the mismatch of currents. Originality/value The results are simulated in 45 nm complementary metal oxide semiconductor generic process design kit (GPDK) technology in cadence virtuoso. The phase noise of the proposed Fractiona-N phase locked loop has–93.18, –101.4 and –117 dBc/Hz at 10 kHz, 100 kHz and 1 MHz frequency offsets, respectively, and consumes 3.3 mW from a 0.45 V supply.