Search Results (26)

This work is focused on the performance of three different standard-cell-based comparator topologies, considering ultra-low-voltage (ULV) operation. The main application scenarios in which standard-cell-based comparators can be exploited are considered, and a set of figures of merit (FoM) to allow an in-depth comparison among the different topologies is introduced. Then, a set of simulation testbenches are defined in order to simulate and compare the considered topologies implemented in both a 130 nm technology and a 28 nm FDSOI CMOS process. Propagation delay, power consumption and power–delay product are evaluated for different values of the input common mode voltage, as a function of input differential amplitude, and in different supply voltage and temperature conditions. Monte Carlo simulations to evaluate the input offset voltage under mismatch variations are also provided. Simulation results show that the performances of the different comparator topologies are strongly dependent on the input common mode voltage, and that the best values for all the performance figures of merit are achieved by the comparator based on three-input NAND gates, with the only limitation being its non-rail-to-rail input common mode range (ICMR). The performances of the considered comparator topologies have also been simulated for different values of the supply voltage, ranging from 0.3 V to 1.2 V, showing that, even if standard-cell-based comparators can be operated at higher supply voltages by scaling their performances accordingly, the best values of the FoMs are achieved for $V_{DD}$ = 0.3 V. Full article

For Convolutional Neural Networks (CNNs), Depthwise Separable CNN (DSCNN) is the preferred architecture for Application Specific Integrated Circuit (ASIC) implementation on edge devices. It benefits from a multi-mode approximate multiplier proposed in this work. The proposed approximate multiplier uses two 4-bit multiplication operations to implement a 12-bit multiplication operation by reusing the same multiplier array. With this approximate multiplier, sequential multiplication operations are pipelined in a modified DSCNN to fully utilize the Processing Element (PE) array in the convolutional layer. Two versions of Approximate-DSCNN (A-DSCNN) accelerators were implemented on TSMC 40 nm CMOS process with a supply voltage of 0.9 V. At a clock frequency of 200 MHz, the designs achieve 4.78 GOPs/mW and 4.89 GOP/mW power efficiency while occupying 1.16 mm${}^2$ and 0.398 mm${}^2$ area, respectively. Full article

In contemporary devices, the number and diversity of sensors is increasing, thus, requiring both efficient and robust interfacing to the sensors. Implementing the interfacing systems in advanced integration technologies faces numerous issues due to manufacturing deviations, signal swings, noise, etc. The interface sensor designers escape to the time domain and digital design techniques to handle these challenges. Biology gives examples of efficient machines that have vastly outperformed conventional technology. This work pursues a neuromorphic spiking sensory system design with the same efficient style as biology. Our chip, that comprises the essential elements of the adaptive neuromorphic spiking sensory system, such as the neuron, synapse, adaptive coincidence detection (ACD), and self-adaptive spike-to-rank coding (SA-SRC), was manufactured in XFAB CMOS 0.35 $\mathsf{\mu }$m technology via EUROPRACTICE. The main emphasis of this paper is to present the measurement outcomes of the SA-SRC on-chip, evaluating the efficacy of its adaptation scheme, and assessing its capability to produce spike orders that correspond to the temporal difference between the two spikes received at its inputs. The SA-SRC plays a crucial role in performing the primary function of the adaptive neuromorphic spiking sensory system. The measurement results of the chip confirm the simulation results of our previous work. Full article

This paper presents a state-of-the-art review of different approaches for Neural Architecture Search targeting resource-constrained devices such as microcontrollers, as well as the implementations of on-device learning techniques for them. Approaches such as MCUNet have been able to drive the design of tiny neural architectures with low memory and computational requirements which can be deployed effectively on microcontrollers. Regarding on-device learning, there are various solutions that have addressed concept drift and have coped with the accuracy drop in real-time data depending on the task targeted, and these rely on a variety of learning methods. For computer vision, MCUNetV3 uses backpropagation and represents a state-of-the-art solution. The Restricted Coulomb Energy Neural Network is a promising method for learning with an extremely low memory footprint and computational complexity, which should be considered for future investigations. Full article

This paper presents a practical implementation and measurement results of power-efficient chip performance optimization, utilizing low-cost indirect measurement methods to support self-X properties (self-calibration, self-healing, self-optimization, etc.) for in-field optimization of analog front-end sensory electronics with XFAB 0.35 µm complementary metal oxide semiconductor (CMOS) technology. The reconfigurable, fully differential indirect current-feedback instrumentation amplifier (CFIA) performance is intrinsically optimized by employing a single test sinusoidal signal stimulus and measuring the total harmonic distortion (THD) at the output. To enhance the optimization process, the experience replay particle swarm optimization (ERPSO) algorithm is utilized as an artificial intelligence (AI) agent, implemented at the hardware level, to optimize the performance characteristics of the CFIA. The ERPSO algorithm extends the selection producer capabilities of the classical PSO methodology by incorporating an experience replay buffer to mitigate the likelihood of being trapped in local optima. Furthermore, the CFIA circuit has been integrated with a simple power-monitoring module to assess the power consumption of the optimization solution, to achieve a power-efficient and reliable configuration. The optimized chip performance showed an approximate 34% increase in power efficiency while achieving a targeted THD value of −72 dB, utilizing a 1 Vp-p differential input signal with a frequency of 1 MHz, and consuming approximately 53 mW of power. Preliminary tests conducted on the fabricated chip, using the default configuration pattern extrapolated from post-layout simulations, revealed an unacceptable performance behavior of the CFIA. Nevertheless, the proposed in-field optimization successfully restored the circuit’s performance, resulting in a robust design that meets the performance achieved in the design phase. Full article

Silicon radiation detectors, a special type of microelectronic sensor which plays a crucial role in many applications, are reviewed in this paper, focusing on fabrication aspects. After addressing the basic concepts and the main requirements, the evolution of detector technologies is discussed, which has been mainly driven by the ever-increasing demands for frontier scientific experiments. Full article

Content-addressable memory (CAM) has been part of the memory market for more than five decades. CAM can carry out a single clock cycle lookup based on the content rather than an address. Thanks to this attractive feature, CAM is utilized in memory systems where a high-speed content lookup technique is required. However, typical CAM applications only support exact matching, as opposed to approximate matching, where a certain Hamming distance (several mismatching characters between a query pattern and the dataset stored in CAM) needs to be tolerated. Recent interest in approximate search has led to the development of new CAM-based alternatives, accelerating the processing of large data workloads in the realm of big data, genomics, and other data-intensive applications. In this review, we provide an overview of approximate CAM and describe its current and potential applications that would benefit from approximate search computing. Full article

This paper reviews the state of the art on bandpass $\Sigma \Delta$ modulators (BP-$\Sigma \Delta$Ms) intended to digitize radio frequency (RF) signals. A priori, this is the most direct way to implement software-defined radio (SDR) systems since the analog/digital interface is placed closer to the antenna, thus reducing the analog circuitry and doing most of the signal processing in the digital domain. In spite of their higher programmability and scalability, RF BP-$\Sigma \Delta$M analog-to-digital converters (ADCs) require more energy to operate in the GHz range as compared with their low-pass (LP) counterparts. This makes conventional direct conversion receivers (DCRs) the commonplace approach due to their overall smaller energy consumption. This paper surveys some circuits and systems techniques which can make RF ADCs and SDR-based transceivers more efficient and feasible to be embedded in mobile terminals. Full article

This paper presents a design methodology for a low-power, low-chip-area, and high-resolution successive approximations register (SAR) analog-to-digital converter (ADC). The proposed method includes a segmented capacitive DAC (C-DAC) to reduce the power consumption and the total area. An embedded self-calibration algorithm based on a set of trimming capacitors was applied alongside a dynamic element matching (DEM) procedure to control the inherent linearity issues caused by the process mismatch. The SAR ADC and each additional algorithm were modeled in MATLAB to show their efficiency. Finally, a simple methodology was developed to allow for the fast estimation of signal-to-noise ratios (SNRs) without any FFT calculation. Full article

Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for a neuromorphic robotic system controlled by a Spiking Neural Network, which is demonstrated on the task of intercepting incoming objects. The data rate of the implemented interface board is 24.64 k symbols/s and the latency for generating commands is about 11ms. The complete system is run only by batteries, making it very suitable for robotic applications. Full article

Inverter-based Operational Transconductance Amplifiers (OTAs) are versatile and friendly scalable analog circuit blocks. Especially for the new CMOS technological nodes, several recent applications have been extensively using them, ranging from Analog Front End (AFE) to analog-to-digital converters (ADC). This work tracks down the current advances in inverter-based OTAs design, comparing their basic fully differential structures, such as Nauta (N), Barthelemy (B), Vieru (V) and Mafredini (M) ones, and, in addition, mixing them up to propose new fully differential single-ended and two-stage hybrid versions. The new herein-proposed fully differential hybrid OTAs are the composition of Barthelemy/Nauta (B/N), Barthelemy/Manfredini (B/M), Nauta/Vieru (N/V), and Manfredini/Vieru (M/V) OTAs. All OTAs were designed using the same Global Foundries 180 nm open-source PDK and their performances are compared for post-layout simulations. Full article

This paper presents an ultra-low power CMOS voltage reference which operates in the subthreshold region. Modified from the conventional 2T circuit, the proposed circuit is capable of generating higher output voltage by using the resistor subdivision. The design comprises a negative-threshold native NMOS transistor as the current generator, a high-threshold PMOS transistor as the active load and an active voltage doubling network to generate the reference voltage. Implemented in TSMC 40 nm CMOS technology, the proposed circuit operates at a minimum supply of 0.65 V and consumes 5.5 nA. Under one sample simulation, the obtained T.C. is 16.64 ppm/°C and the nominal $V_{ref}$ is 489.6 mV (75.3% of $V_{ddmin}$) for the temperature range from −20 °C to 80 °C. For Monte-Carlo simulation of 200 samples at room temperature, the average output voltage is 488 mV and the average T.C. is 29.6 ppm/°C whilst with the standard deviation of 13.26 ppm/°C. Finally, at room temperature, the proposed voltage reference has achieved a process sensitivity (σ/μ) of 3.9%, a line sensitivity of 0.51%/V and a power supply rejection of −45.5 dB and −76.3 dB at 100 kHz and 100 MHz. Compared to the representative prior-art works realized in the same technology and a similar supply current, the proposed circuit has offered the best 1-sampe T.C., the best average T.C. in multiple samples, the highest output voltage, the maximum output voltage per minimum supply voltage and the lowest process sensitivity in the output, $V_{ref}$. Full article

This Communication reports on FPGA prototyping of an embedded web service that sends XML messages under two different packages, namely Simple Object Access Protocol (SOAP) and Representational State Transfer (REST). The request and response messages are communicated through a 100 Mbps local area network between a Spartan-3E FPGA board and washing machine simulator. The performances of REST-based and SOAP-based web services implemented on reconfigurable hardware are then compared. In general, the former performs better than the latter in terms of FPGA resource utilization (~12% less), message length (~57% shorter), and processing time (~4.5 μs faster). This work confirms the superiority of REST over SOAP for data transmission using reconfigurable computing, which paves the way for adoption of these low-cost systems for web services of consumer electronics such as home appliances. Full article

The endurability of solder joints in the ball-grid array (BGA) packaging is crucial to the functioning of the microelectronic system. To improve electronic packaging reliability, this paper is dedicated to numerically optimize solder joint array configuration and study the influence of multi-physical fields on solder joint reliability. The uniqueness of this study is that on the basis of temperature field and stress field, the electric field is added to realize the coupling simulation of three physical fields. In addition, the “Open Angle” is mathematically defined to describe the array configuration, and it was used to reveal the influence factors of solder joint fatigue, including stress, temperature, and current density. In the single solder joint model, the impacts of geometric shape and working conditions on the maximum value and distribution of these evaluation factors (stress, temperature, and current density) were investigated. Overall, the numerical investigation gives the optimal configuration, geometric shape, and working condition of solder joints, which benefits the design of endurable and efficient BGA packaging. Full article

In this work, we present a compact “adaptive downsampling” method that mitigates the nonlinearity problems associated with FPGA-based TDCs that utilize delay lines. Additionally, this generic method allows for trade-offs between resolution, linearity, and resource utilization. Since nonlinearity is one of the predominant issues regarding delay lines in FPGA-based TDCs, combined with the fact that delay lines are utilized for a wide range of TDC architectures (not limited to the delay-line TDC), other implementations (e.g., Vernier or wave union TDCs), also in different FPGA devices, can directly benefit from the proposed adaptive method, with no need for either custom routing or complex tuning of the converter. Furthermore, implementation-related challenges regarding clock skew, measurement uncertainty, and the placement of the TDC are discussed and we also propose an experimental setup that utilizes only FPGA resources in order to characterize the converter. Although the TDC in this work was implemented in a Xilinx Virtex-6 device and was characterized under different operational modes, we successfully optimized the converter’s nonlinearity and resource utilization while retaining single-shot precision. The best performing (in terms of linearity) implementation reached $DN{L}_{rms}$ and $IN{L}_{rms}$ values of 0.30 LSB and 0.45 LSB, respectively, and the single-shot precision ($\sigma$) was 9.0 ps. Full article