Towards ultra-low power consumption VAD architectures with mixed signal circuits

Proceedings of: 56th Edition IEEE ISCAS 2023 - IEEE International Symposium on Circuits and Systems (ISCAS), 21-25 May 2023, Monterey, CA, USA.A voice activity detector architecture based on an analog feature extractor and a mixed signal classification stage is proposed for ultra-low power activity. The feature extraction stage is composed of a set of analog band-pass filters and frame energy estimators. The classification stage has a fully connected first layer built with ultra-low power consumption ring oscillators, followed by gated recurrent unit layers. The ring oscillator based layer consumes nWs according to transient simulations performed in a low power 65 nm CMOS technology. Additionally it features the ability to perform the analog-to-digital conversion required to handle subsequent GRU layers, as well as the possibility of computing a non-linear function like sigmoid seizing the intrinsic non-linearity of the ring oscillator. Training and testing operations are made proving competitive classification performance between a baseline model and our proposed architecture. In light of this, proper features for deployment on power-restricted edge-computing applications are shown.This paper was supported by program H2020-MSCA-ITN-2020 grant Nr.956601

Mixed-Signal Testability Analysis for Data-Converter IPs

In this paper, a new procedure to derive testability measures is presented. Digital testability can be calculated by means of probability, while in analog it is possible to calculate testability using impedance values. Although attempts have been made to reach compatibility, matching was somewhat arbitrary and therefore not necessarily compatible. The concept of the new approach is that digital and analog can be integrated in a more consistent way. More realistic testability figures are obtained, which makes testability of true mixed-signal systems and circuits feasible. To verify the results, our method is compared with a sensitivity analysis, for a simple 3-bit ADC

Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever-increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks and open problems in the field of neuro-memristive circuits for edge computing

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system