Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can model neural activity patterns with points in a HD space, that is, with HD vectors. Key examined properties of ...HD computing include: a versatile set of arithmetic operations on HD vectors, generality, scalability, analyzability, one-shot learning, and energy efficiency. These make it a prime candidate for efficient biosignal processing where signals are noisy and nonstationary, training data sets are not huge, individual variability is significant, and energy-efficiency constraints are tight. Purely based on native HD computing operators, we describe a combined method for multiclass learning and classification of various ExG biosignals such as electromyography (EMG), electroencephalography (EEG), and electrocorticography (ECoG). We develop a full set of HD network templates that comprehensively encode body potentials and brain neural activity recorded from different electrodes into a single HD vector without requiring domain expert knowledge or ad hoc electrode selection process. Such encoded HD vector is processed as a single unit for fast one-shot learning, and robust classification. It can be interpreted to identify the most useful features as well. Compared to state-of-the-art counterparts, HD computing enables online, incremental, and fast learning as it demands less than a third as much training data as well as less preprocessing.
The emerging field of bioelectronic medicine seeks methods for deciphering and modulating electrophysiological activity in the body to attain therapeutic effects at target organs. Current approaches ...to interfacing with peripheral nerves and muscles rely heavily on wires, creating problems for chronic use, while emerging wireless approaches lack the size scalability necessary to interrogate small-diameter nerves. Furthermore, conventional electrode-based technologies lack the capability to record from nerves with high spatial resolution or to record independently from many discrete sites within a nerve bundle. Here, we demonstrate neural dust, a wireless and scalable ultrasonic backscatter system for powering and communicating with implanted bioelectronics. We show that ultrasound is effective at delivering power to mm-scale devices in tissue; likewise, passive, battery-less communication using backscatter enables high-fidelity transmission of electromyogram (EMG) and electroneurogram (ENG) signals from anesthetized rats. These results highlight the potential for an ultrasound-based neural interface system for advancing future bioelectronics-based therapies.
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•First in vivo electrophysiological recordings with neural dust motes•Passive, wireless, and battery-less EMG and ENG recording with mm-scale devices•Recorded signals transmitted via ultrasonic backscatter from implanted neural dust motes•Ultrasound as a scalable means of providing wireless power and communication
Ultrasound can be used to wirelessly power and communicate with mm-scale devices implanted in the body. Seo, Neely et al. demonstrate wireless recordings from nerve (ENG) and muscle (EMG) in anesthetized rats using neural dust—a battery-less ultrasonic backscattering system.
Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HD ...representations when applied to pattern recognition tasks. Particular design choices include density of representations and strategies for mapping data from the original representation. It is demonstrated that for the considered pattern recognition tasks (using synthetic and real-world data) both sparse and dense representations behave nearly identically. This paper also discusses implementation peculiarities which may favor one type of representations over the other. Finally, the capacity of representations of various densities is discussed.
Human-Centric Computing Rabaey, Jan M.
IEEE transactions on very large scale integration (VLSI) systems,
2020-Jan., 2020-1-00, 20200101, Letnik:
28, Številka:
1
Journal Article
Recenzirano
With the world around us rapidly becoming smarter, an extremely relevant question is how "we humans" are going to cope with the onslaught of information coming at us. One plausible answer is to use ...similar technologies to evolve ourselves and to equip us with the necessary tools to interact with and to become an essential part of the smart world. Various wearable devices have been or are being developed for this purpose. However, their potential to create a whole new set of human experiences is still largely unexplored. To be more effective, functionality cannot be centralized and needs to be distributed to capture the right information at the right place. This requires a human Intranet, a platform that allows multiple distributed input-output and information processing functions to coalesce and form a single application. In addition, it needs the capabilities to understand, interpret, reason, and act on the obtained data under diverse and changing conditions, and to do so in concert with the human body and its computer, the brain. To this effect, this article explores the concept of human-centric computing, an approach that aspires to create a symbiotic convergence between biological and physical computing.
Memory-augmented neural networks enhance a neural network with an external key-value (KV) memory whose complexity is typically dominated by the number of support vectors in the key memory. We propose ...a generalized KV memory that decouples its dimension from the number of support vectors by introducing a free parameter that can arbitrarily add or remove redundancy to the key memory representation. In effect, it provides an additional degree of freedom to flexibly control the tradeoff between robustness and the resources required to store and compute the generalized KV memory. This is particularly useful for realizing the key memory on in-memory computing hardware where it exploits nonideal, but extremely efficient nonvolatile memory devices for dense storage and computation. Experimental results show that adapting this parameter on demand effectively mitigates up to 44% nonidealities, at equal accuracy and number of devices, without any need for neural network retraining.
Wireless power transfer (WPT) has long been one of the main goals of Nikola Tesla, the forefather of electromagnetic applications. In this paper, we investigate radio-frequency beamforming in the ...radiative far field for WPT. First, an analytical model of the channel fading is presented, and a blind adaptive beamforming algorithm is adapted to the WPT context. The algorithm is computationally light, because we need not explicitly estimate the channel state information. Then, a testbed with a multiple-antenna software-defined radio configuration on the transmitting side and a programmable energy harvester on the receiving side is developed in order to validate the algorithm in this specific power application. From the results, it can be seen that the implementation of this version of beamforming indeed improves the harvested power. Specifically, at various distances from 50 cm to 1.5 m, the algorithm converges with two, three, and four antennas with an increasing gain as we increase the number of antennas. These encouraging results could have far-reaching consequences in providing wireless power to Internet of Things devices, our target application.
We outline a model of computing with high-dimensional (HD) vectors-where the dimensionality is in the thousands. It is built on ideas from traditional (symbolic) computing and artificial neural ...nets/deep learning, and complements them with ideas from probability theory, statistics, and abstract algebra. Key properties of HD computing include a well-defined set of arithmetic operations on vectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operation, making it possible to develop efficient algorithms for large-scale real-world tasks. We present a 2-D architecture and demonstrate its functionality with examples from text analysis, pattern recognition, and biosignal processing, while achieving high levels of classification accuracy (close to or above conventional machine-learning methods), energy efficiency, and robustness with simple algorithms that learn fast. HD computing is ideally suited for 3-D nanometer circuit technology, vastly increasing circuit density and energy efficiency, and paving a way to systems capable of advanced cognitive tasks.
Adaptive Body Area Networks Using Kinematics and Biosignals Moin, Ali; Thielens, Arno; Araujo, Alvaro ...
IEEE journal of biomedical and health informatics,
2021-March, 2021-Mar, 2021-3-00, 20210301, Letnik:
25, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around ...the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.
Operation in the subthreshold region most often is synonymous to minimum-energy operation. Yet, the penalty in performance is huge. In this paper, we explore how design in the moderate inversion ...region helps to recover some of that lost performance, while staying quite close to the minimum-energy point. An energy-delay modeling framework that extends over the weak, moderate, and strong inversion regions is developed. The impact of activity and design parameters such as supply voltage and transistor sizing on the energy and performance in this operational region is derived. The quantitative benefits of operating in near-threshold region are established using some simple examples. The paper shows that a 20% increase in energy from the minimum-energy point gives back ten times in performance. Based on these observations, a pass-transistor based logic family that excels in this operational region is introduced. The logic family operates most of its logic in the above-threshold mode (using low-threshold transistors), yet containing leakage to only those in subthreshold. Operation below minimum-energy point of CMOS is demonstrated. In leakage-dominated ultralow-power designs, time-multiplexing will be shown to yield not only area, but also energy reduction due to lower leakage. Finally, the paper demonstrates the use of ultralow-power design techniques in chip synthesis.
•We present neural dust: a method to power and communicate with sub-mm neural motes.•We provide theory, modeling, and simulation of ultrasound-based neural dust.•We provide experimental verification ...of the predicted scaling effects.•We discuss using neural dust for central and peripheral nervous system recordings.
A major hurdle in brain–machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a substantial fraction of the user's lifetime. Recently, sub-mm implantable, wireless electromagnetic (EM) neural interfaces have been demonstrated in an effort to extend system longevity. However, EM systems do not scale down in size well due to the severe inefficiency of coupling radio-waves at those scales within tissue. This paper explores fundamental system design trade-offs as well as size, power, and bandwidth scaling limits of neural recording systems built from low-power electronics coupled with ultrasonic power delivery and backscatter communication. Such systems will require two fundamental technology innovations: (1) 10–100μm scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data via ultrasonic backscattering and (2) a sub-cranial ultrasonic interrogator that establishes power and communication links with the neural dust. We provide experimental verification that the predicted scaling effects follow theory; (127μm)3 neural dust motes immersed in water 3cm from the interrogator couple with 0.002064% power transfer efficiency and 0.04246ppm backscatter, resulting in a maximum received power of ∼0.5μW with ∼1nW of change in backscatter power with neural activity. The high efficiency of ultrasonic transmission can enable the scaling of the sensing nodes down to 10s of micrometer. We conclude with a brief discussion of the application of neural dust for both central and peripheral nervous system recordings, and perspectives on future research directions.
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