1
|
Kleyko D, Karunaratne G, Rabaey JM, Sebastian A, Rahimi A. Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks. IEEE Trans Neural Netw Learn Syst 2023; 34:10993-10998. [PMID: 35333724 DOI: 10.1109/tnnls.2022.3159445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
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.
Collapse
|
2
|
Kleyko D, Davies M, Frady EP, Kanerva P, Kent SJ, Olshausen BA, Osipov E, Rabaey JM, Rachkovskij DA, Rahimi A, Sommer FT. Vector Symbolic Architectures as a Computing Framework for Emerging Hardware. Proc IEEE Inst Electr Electron Eng 2022; 110:1538-1571. [PMID: 37868615 PMCID: PMC10588678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, emerging hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the field-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on high-dimensional vectors that can support all data structures and manipulations relevant to modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. It also opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. We sketch ways of demonstrating that Vector Symbolic Architectures are computationally universal. We see them acting as a framework for computing with distributed representations that can play a role of an abstraction layer for emerging computing hardware. This article serves as a reference for computer architects by illustrating the philosophy behind Vector Symbolic Architectures, techniques of distributed computing with them, and their relevance to emerging computing hardware, such as neuromorphic computing.
Collapse
Affiliation(s)
- Denis Kleyko
- Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA and also with the Intelligent Systems Lab at Research Institutes of Sweden, 16440 Kista, Sweden
| | - Mike Davies
- Neuromorphic Computing Lab, Intel Labs, Santa Clara, CA 95054, USA
| | - E Paxon Frady
- Neuromorphic Computing Lab, Intel Labs, Santa Clara, CA 95054, USA
| | - Pentti Kanerva
- Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA
| | - Spencer J Kent
- Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA
| | - Bruno A Olshausen
- Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA
| | - Evgeny Osipov
- Department of Computer Science Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
| | - Jan M Rabaey
- Department of Electrical Engineering and Computer Sciences at the University of California at Berkeley, CA 94720, USA
| | - Dmitri A Rachkovskij
- International Research and Training Center for Information Technologies and Systems, 03680 Kyiv, Ukraine, and with the Department of Computer Science Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
| | - Abbas Rahimi
- IBM Research - Zurich, 8803 Rüschlikon, Switzerland
| | - Friedrich T Sommer
- Neuromorphic Computing Lab, Intel Labs, Santa Clara, CA 95054, USA and also with the Redwood Center for Theoretical Neuroscience at the University of California at Berkeley, CA 94720, USA
| |
Collapse
|
3
|
Menon A, Sun D, Sabouri S, Lee K, Aristio M, Liew H, Rabaey JM. A Highly Energy-Efficient Hyperdimensional Computing Processor for Biosignal Classification. IEEE Trans Biomed Circuits Syst 2022; 16:524-534. [PMID: 35776812 DOI: 10.1109/tbcas.2022.3187944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Hyperdimensional computing (HDC) is a brain-inspired computing paradigm that operates on pseudo-random hypervectors to perform high-accuracy classifications for biomedical applications. The energy efficiency of prior HDC processors for this computationally minimal algorithm is dominated by costly hypervector memory storage, which grows linearly with the number of sensors. To address this, the memory is replaced with a light-weight cellular automaton for on-the-fly hypervector generation. The use of this technique is explored in conjunction with vector folding for various real-time classification latencies in post-layout simulation on an emotion recognition dataset with 200 channels. The proposed architecture achieves 39.1 nJ/prediction; a 4.9× energy efficiency improvement, 9.5× per channel, over the state-of-the-art HDC processor. At maximum throughput, the architecture achieves a 10.7× improvement, 33.5× per channel. An optimized support vector machine (SVM) processor is designed in this work for the same use-case. HDC is 9.5× more energy-efficient than the SVM, paving the way for it to become the paradigm of choice for high-accuracy, on-board biosignal classification.
Collapse
|
4
|
Menon A, Natarajan A, Agashe R, Sun D, Aristio M, Liew H, Shao YS, Rabaey JM. Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata. Brain Inform 2022; 9:14. [PMID: 35759153 PMCID: PMC9237202 DOI: 10.1186/s40708-022-00162-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/15/2022] [Indexed: 12/02/2022] Open
Abstract
In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human–computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of > 76% for valence and > 73% for arousal on the multi-modal AMIGOS and DEAP data sets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.
Collapse
Affiliation(s)
- Alisha Menon
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA.
| | - Anirudh Natarajan
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
| | - Reva Agashe
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
| | - Daniel Sun
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
| | - Melvin Aristio
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
| | - Harrison Liew
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
| | - Yakun Sophia Shao
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA
| | - Jan M Rabaey
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA.
| |
Collapse
|
5
|
Moin A, Thielens A, Araujo A, Sangiovanni-Vincentelli A, Rabaey JM. Adaptive Body Area Networks Using Kinematics and Biosignals. IEEE J Biomed Health Inform 2021; 25:623-633. [PMID: 32749974 DOI: 10.1109/jbhi.2020.3003924] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
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.
Collapse
|
6
|
Kleyko D, Rahimi A, Rachkovskij DA, Osipov E, Rabaey JM. Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics. IEEE Trans Neural Netw Learn Syst 2018; 29:5880-5898. [PMID: 29993669 DOI: 10.1109/tnnls.2018.2814400] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
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.
Collapse
|
7
|
Thielens A, Benarrouch R, Wielandt S, Anderson MG, Moin A, Cathelin A, Rabaey JM. A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz⁻2.4 GHz Range. Sensors (Basel) 2018; 18:E4165. [PMID: 30486453 PMCID: PMC6308834 DOI: 10.3390/s18124165] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 11/14/2018] [Accepted: 11/16/2018] [Indexed: 11/21/2022]
Abstract
While there exists a wide variety of radio frequency (RF) technologies amenable for usage in Wireless Body Area Networks (WBANs), which have been studied separately before, it is currently still unclear how their performance compares in true on-body scenarios. In this paper, a single reference on-body scenario-that is, propagation along the arm-is used to experimentally compare six distinct RF technologies (between 420 MHz and 2.4 GHz) in terms of path loss. To further quantify on-body path loss, measurements for five different on-body scenarios are presented as well. To compensate for the effect of often large path losses, two mitigation strategies to (dynamically) improve on-body links are introduced and experimentally verified: beam steering using a phased array, and usage of on-body RF repeaters. The results of this study can serve as a tool for WBAN designers to aid in the selection of the right RF frequency and technology for their application.
Collapse
Affiliation(s)
- Arno Thielens
- Berkeley Wireless Research Center, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94704, USA.
- Waves Research Group, IMEC, Department of Information Technology, Ghent University, 9052 Ghent, Belgium.
| | - Robin Benarrouch
- Berkeley Wireless Research Center, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94704, USA.
- CNRS, Centrale Lille, ISEN, University Valenciennes, UMR 8520-IEMN, University Lille, F-59000 Lille, France.
- STMicroelectronics, Technology and Design Platforms, 38920 Crolles, France.
| | - Stijn Wielandt
- Berkeley Wireless Research Center, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94704, USA.
- DRAMCO, Department of Electrical Engineering (ESAT), Ghent Technology Campus, KU Leuven, 9000 Ghent, Belgium.
| | - Matthew G Anderson
- Berkeley Wireless Research Center, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94704, USA.
| | - Ali Moin
- Berkeley Wireless Research Center, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94704, USA.
| | - Andreia Cathelin
- STMicroelectronics, Technology and Design Platforms, 38920 Crolles, France.
| | - Jan M Rabaey
- Berkeley Wireless Research Center, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, Berkeley, CA 94704, USA.
| |
Collapse
|
8
|
Carmena JM, Rabaey JM, Alon E, Boser BE, Maharbiz MM. Ultrasonic beamforming system for interrogating multiple implantable sensors. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2015:2673-6. [PMID: 26736842 DOI: 10.1109/embc.2015.7318942] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, we present an ultrasonic beamforming system capable of interrogating individual implantable sensors via backscatter in a distributed, ultrasound-based recording platform known as Neural Dust [1]. A custom ASIC drives a 7 × 2 PZT transducer array with 3 cycles of 32V square wave with a specific programmable time delay to focus the beam at the 800mm neural dust mote placed 50mm away. The measured acoustic-to-electrical conversion efficiency of the receive mote in water is 0.12% and the overall system delivers 26.3% of the power from the 1.8V power supply to the transducer drive output, consumes 0.75μJ in each transmit phase, and has a 0.5% change in the backscatter per volt applied to the input of the backscatter circuit. Further miniaturization of both the transmit array and the receive mote can pave the way for a wearable, chronic sensing and neuromodulation system.
Collapse
|
9
|
Moin A, Alexandrov G, Johnson BC, Izyumin I, Burghardt F, Shah K, Pannu S, Alon E, Muller R, Rabaey JM. Powering and communication for OMNI: A distributed and modular closed-loop neuromodulation device. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2016:4471-4474. [PMID: 28269271 DOI: 10.1109/embc.2016.7591720] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A distributed, modular, intelligent, and efficient neuromodulation device, called OMNI, is presented. It supports closed-loop recording and stimulation on 256 channels from up to 4 physically distinct neuromodulation modules placed in any configuration around the brain, hence offering the capability of addressing neural disorders that are presented at the network level. The specific focus of this paper is the communication and power distribution network that enables the modular and distributed nature of the device.
Collapse
|
10
|
Seo D, Neely RM, Shen K, Singhal U, Alon E, Rabaey JM, Carmena JM, Maharbiz MM. Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust. Neuron 2016; 91:529-39. [DOI: 10.1016/j.neuron.2016.06.034] [Citation(s) in RCA: 316] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Revised: 03/30/2016] [Accepted: 06/21/2016] [Indexed: 11/15/2022]
|
11
|
Bertrand A, Seo D, Maksimovic F, Carmena JM, Maharbiz MM, Alon E, Rabaey JM. Beamforming approaches for untethered, ultrasonic neural dust motes for cortical recording: a simulation study. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2014:2625-8. [PMID: 25570529 DOI: 10.1109/embc.2014.6944161] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this paper, we examine the use of beamforming techniques to interrogate a multitude of neural implants in a distributed, ultrasound-based intra-cortical recording platform known as Neural Dust. We propose a general framework to analyze system design tradeoffs in the ultrasonic beamformer that extracts neural signals from modulated ultrasound waves that are backscattered by free-floating neural dust (ND) motes. Simulations indicate that high-resolution linearly-constrained minimum variance beamforming sufficiently suppresses interference from unselected ND motes and can be incorporated into the ND-based cortical recording system.
Collapse
|
12
|
Chu P, Muller R, Koralek A, Carmena JM, Rabaey JM, Gambini S. Equalization for intracortical microstimulation artifact reduction. Annu Int Conf IEEE Eng Med Biol Soc 2015; 2013:245-8. [PMID: 24109670 DOI: 10.1109/embc.2013.6609483] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We present a method for decreasing the duration of artifacts present during intra-cortical microstimulation (ICMS) recordings by using techniques developed for digital communications. We replace the traditional monophasic or biphasic current stimulation pulse with a patterned pulse stream produced by a Zero Forcing Equalizer (ZFE) filter after characterizing the artifact as a communications channel. The results find that using the ZFE stimulus has the potential to reduce artifact width by more than 70%. Considerations for the hardware implementation of the equalizer are presented.
Collapse
|
13
|
Marblestone AH, Zamft BM, Maguire YG, Shapiro MG, Cybulski TR, Glaser JI, Amodei D, Stranges PB, Kalhor R, Dalrymple DA, Seo D, Alon E, Maharbiz MM, Carmena JM, Rabaey JM, Boyden ES, Church GM, Kording KP. Physical principles for scalable neural recording. Front Comput Neurosci 2013; 7:137. [PMID: 24187539 PMCID: PMC3807567 DOI: 10.3389/fncom.2013.00137] [Citation(s) in RCA: 129] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2013] [Accepted: 09/23/2013] [Indexed: 12/20/2022] Open
Abstract
Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power-bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.
Collapse
Affiliation(s)
- Adam H. Marblestone
- Biophysics Program, Harvard UniversityBoston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard UniversityBoston, MA, USA
| | | | - Yael G. Maguire
- Department of Genetics, Harvard Medical SchoolBoston, MA, USA
- Plum Labs LLCCambridge, MA, USA
| | - Mikhail G. Shapiro
- Division of Chemistry and Chemical Engineering, California Institute of TechnologyPasadena, CA, USA
| | | | - Joshua I. Glaser
- Interdepartmental Neuroscience Program, Northwestern UniversityChicago, IL, USA
| | - Dario Amodei
- Department of Radiology, Stanford UniversityPalo Alto, CA, USA
| | | | - Reza Kalhor
- Department of Genetics, Harvard Medical SchoolBoston, MA, USA
| | - David A. Dalrymple
- Biophysics Program, Harvard UniversityBoston, MA, USA
- NemaloadSan Francisco, CA, USA
- Media Laboratory, Massachusetts Institute of TechnologyCambridge, MA, USA
| | - Dongjin Seo
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Elad Alon
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Michel M. Maharbiz
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Jose M. Carmena
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
- Helen Wills Neuroscience Institute, University of California at BerkeleyBerkeley, CA, USA
| | - Jan M. Rabaey
- Department of Electrical Engineering and Computer Sciences, University of California at BerkeleyBerkeley, CA, USA
| | - Edward S. Boyden
- Media Laboratory, Massachusetts Institute of TechnologyCambridge, MA, USA
- Departments of Brain and Cognitive Sciences and Biological Engineering, Massachusetts Institute of TechnologyCambridge, MA, USA
| | - George M. Church
- Biophysics Program, Harvard UniversityBoston, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard UniversityBoston, MA, USA
- Department of Genetics, Harvard Medical SchoolBoston, MA, USA
| | - Konrad P. Kording
- Departments of Physical Medicine and Rehabilitation and of Physiology, Northwestern University Feinberg School of MedicineChicago, IL, USA
- Sensory Motor Performance Program, The Rehabilitation Institute of ChicagoChicago, IL, USA
| |
Collapse
|
14
|
Mark M, Bjorninen T, Chen YD, Venkatraman S, Ukkonen L, Sydanheimo L, Carmena JM, Rabaey JM. Wireless channel characterization for mm-size neural implants. Annu Int Conf IEEE Eng Med Biol Soc 2011; 2010:1565-8. [PMID: 21096382 DOI: 10.1109/iembs.2010.5626695] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper discusses an approach to modeling and characterizing wireless channel properties for mm-size neural implants. Full-wave electromagnetic simulation was employed to model signal propagation characteristics in biological materials. Animal tests were carried out, proving the validity of the simulation model over a wide range of frequency from 100MHz to 6GHz. Finally, effects of variability and uncertainty in human anatomy and dielectric properties of tissues on these radio links are explored.
Collapse
Affiliation(s)
- Michael Mark
- EECS Department at the University of California at Berkeley, CA 94706, USA
| | | | | | | | | | | | | | | |
Collapse
|
15
|
Abstract
For a moderate-size, multi-hop, sensor network, we present experimental measurements of radio energy consumption and packet reliability. We categorize the energy measurements by energy consumed in each radio state and for each traffic type. Packet reliability results are presented from a network and link perspective, whereas prior work only considered the former. We introduce a novel technique of application-aware radio duty cycling called on-demand spatial TDMA. When compared to the non-cycling case, this technique can achieve greater than an order of magnitude reduction in idle energy consumption, while not sacrificing reliability. We show end-to-end packet loss rates as low as 0.04 when averaged over the network. Even with substantial idle energy savings, we identify radio idling as the dominate energy consumer and overhearing as the dominate traffic type.
Collapse
|