Chips, Volume 2, Issue 2 (June 2023) – 5 articles

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