IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Resistive Random Access Memory (RRAM)-based FPGAs are predicted to outperform conventional FPGAs architectures in area, delay and power over a wide range of voltage operations, allowing novel energy-quality trade-offs for reconfigurable computing. The opportunity lies in that RRAMs can realize the functionality of a Static Random Access Memory (SRAM) and a transmission-gate in a unique device. How... View full abstract»

A common scheme to let a very large number of lowresources sensing units communicate their readings to a remote concentrator is to deploy intermediate hubs that collect subsets of readings by means of local communication and perform the needed long-range transmission of a compressed version of the data. We here propose to exploit Compressed Sensing as an extremely lightweight lossy compression sta... View full abstract»

Approximate arithmetic has recently emerged as a promising paradigm for many imprecision-tolerant applications. It can offer substantial reductions in circuit complexity, delay and energy consumption by relaxing accuracy requirements. In this paper, we propose a novel energy-efficient approximate multiplier design using a significance-driven logic compression (SDLC) approach. Fundamental to this a... View full abstract»

A low-power scalable 3D face frontalization processor is proposed for accurate face recognition in mobile devices. In spite of recent improvement in face recognition accuracy mainly from convolutional neural networks (CNN), their performance is limited to face images with frontal view. For face recognition with human-level accuracy in real life environment, in which most of the face images are cap... View full abstract»

The intrinsic error tolerance of neural network (NN) presents opportunities for approximate computing techniques to improve the energy efficiency of NN inference. Conventional approximate computing focuses on exploiting the efficiencyaccuracy trade-off in existing pre-trained networks, which can lead to suboptimal solutions. In this paper, we first present AxTrain, a hardware-oriented training fra... View full abstract»

Biomedical applications often require classifiers that are both accurate and cheap to implement. Today, deep neural networks achieve state-of-the-art accuracy in most learning tasks that involve large datasets of unstructured data. However, the application of deep learning techniques may not be beneficial in problems with limited training sets and computational resources, or under domain-specific ... View full abstract»

This paper introduces an accuracy/energy-flexible configurable 2D Gabor filter based on stochastic computation, where stochastic bit stream representing information is used. The Gabor filters show a powerful feature extraction capability, but the calculation based on binary computation is complicated. As opposed to traditional memory-based methods that use fixed Gabor coefficients calculated by so... View full abstract»

Outstanding seizure detection algorithms have been developed over past two decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. These works are mainly based on heavily handcrafted feature extraction, which is computationally expensive and is shown to be dataset specific. These issues greatly limit the applicability of such methods to ha... View full abstract»

The emergence of post-silicon nano-devices and the coming trillion-sensor era driven by the Internet of Things (IoT) have led to a search for alternative computational paradigms that can efficiently derive useful information from abundant data while enable efficient hardware implementations under significant device variations. Such computational paradigms may emerge as we explore the information p... View full abstract»

Recently, there has been an increasing demand for advanced classification capabilities embedded on wearable battery constrained devices, such as smartphones or -watches. Achieving such functionality with a tight power and energy budget has proven a real challenge, specifically for large-scale Neural Network based applications. Previously, cascaded systems have been proposed to minimize energy cons... View full abstract»

This paper presents a novel technique to reduce energy consumption of a machine learning classifier based on incremental-precision feature computation and classification. Specifically, the algorithm starts with features computed using the lowest possible precision. Depending on the classification accuracy, the features of the previous level are combined with features of the incremental-precision t... View full abstract»

Deep Neural Networks (DNNs) have emerged as the state-of-the-art technique in a wide range of machine learning tasks for analytics and computer vision in the next generation of embedded (mobile, IoT, wearable) devices. Despite their success, they suffer from high energy requirements. In recent years, the inherent error resiliency of DNNs has been exploited by introducing approximations at either t... View full abstract»

As a rapid progress in technology processes, the design integration of high-performance system-on-chip (SoC) is on the rise rapidly. To incorporate hundreds of IP cores into a single chip, a modern SoC exceeds ten million gates with a large number of scan cells, so that it leads excessive energy consumption. In this paper, we present an energy-quality (EQ) scalable scan test method using new scan ... View full abstract»

This paper presents a low power reconfigurable IC for wearable health devices that perform bio-impedance (BioZ) spectroscopy, respiration and ECG signals measurement. The IC consists of a broadband excitation current source (I-DAC) and a power efficient readout circuit (RO). Both are highly scalable to mitigate the tradeoff among noise, dynamic range, speed, power and safe current limits. On circu... View full abstract»

This paper presents a structure simplification procedure that allows efficient energy and accuracy tradeoffs in implementation of trained deep neural networks (DNNs). This structure simplification procedure identifies and eliminates redundant neurons in any layer of the DNN based on the output values of these neurons over the training samples. This procedure may be applied to all layers of a DNN. ... View full abstract»

Numerous approximate adders have been proposed in the literature in response to the languishing benefits of technology scaling. However, they have been obtained with an adhoc and non-systematic methodology which does not fully exploit the design space possibilities. This work provides a conceptual framework for the systematic design of approximate adders including hybrid and non-equally segmented ... View full abstract»

Wireless image sensor nodes are required deliver better visual information of the region-of-interest (ROI) under tight energy constraints. The energy-quality scalability of the sensor node can be improved by incorporating ROI-based image processing. This paper presents an energy-quality scalable wireless image sensor node using ROI-based processing for object-based surveillance. After detecting th... View full abstract»

Approximate computing has been considered to improve the accuracy-performance trade-off in error-tolerant applications. For many of these applications, multiplication is a key arithmetic operation. Given that approximate compressors are a key element in the design of power-efficient approximate multipliers, we first propose an initial approximate 4:2 compressor that introduces a rather large error... View full abstract»

Emerging millimeter-wave (mmW) wireless systems require beamforming and multiple-input multiple-output (MIMO) approaches in order to mitigate path loss, obstructions, and attenuation of the communication channel. Sharp mmW beams are essential for this purpose and must support baseband bandwidths of at least 1 GHz to facilitate higher system capacity. This paper explores a baseband multi-beamformin... View full abstract»

The progress of Parkinson’s disease (PD) in patients is conventionally monitored through follow-up visits. These may be insufficient for clinicians to obtain a good understanding of the occurrence and severity of symptoms in order to adjust therapy to the patients’ needs. Portable platforms for PD diagnostics can provide in-depth information, thus reducing the frequency of face-to-fa... View full abstract»

This paper presents an energy-efficient and high throughput architecture for convolutional neural networks (CNN). Architectural and circuit techniques are proposed to address the dominant energy and delay costs associated with data movement in CNNs. The proposed architecture employs a deep in-memory architecture (DIMA), to embed energy-efficient low swing mixed-signal computations in the periphery... View full abstract»

We report an always-on event-driven asynchronous wake-up circuit with trainable pattern recognition capabilities to duty-cycle power-constrained internet-of-things (IoT) sensor nodes. The wake-up circuit is based on a level-crossing analog-to-digital converter (LC-ADC) employed as feature-extraction block with automatic activity-sampling rate scaling behavior. A novel asynchronous digital logic cl... View full abstract»

In mobile multimedia devices with video compression capability, a large amount of power consumption is incurred by storing video data in SRAMs. The power consumption by SRAM access is reduced by decreasing the supply voltage but the decreased supply voltage may cause data loss stored in the SRAM. To reduce the probability of data loss, previous research attempts to increase the SRAM cell size, whi... View full abstract»

This paper reports a design for a complementary metal oxide semiconductor (CMOS) microelectromechanical system accelerometer used in Internet of Things (IoT) devices for seismic detection. The proposed device performs the main functions of accelerometers, and has the advantage of automatically selecting the electrostatic force of feedback to null the position of the sensor. This study applied 0.35... View full abstract»

In spintronic-based neuromorphic computing systems (NCS), the switching of magnetic moment in a magnetic tunnel junction (MTJ) is used to mimic neuron firing. However, the stochastic switching behavior of the MTJ and process variations effect lead to a significant increase in stimulation time of such NCSs. Moreover, current NCSs need an extra phase to read the MTJ state after stimulation which is ... View full abstract»