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2024 BrainChip Discussion, page-9758

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    BringingTouch to the Edge: A Neuromorphic

    ProcessingApproach For Event-Based Tactile

    Systems

    Harshil Patel

    Brainchip Research Institute

    Perth, Australia

    Email: [email protected]

    Anup Vanarse

    Brainchip Research Institute

    Perth, Australia

    Email: [email protected]

    Kristofor D. Carlson

    Brainchip Inc.

    Laguna Hills, USA

    Email: [email protected]

    Adam Osseiran

    Brainchip Research Institute

    University of Western Australia

    Perth, Australia

    Email: [email protected]

    Abstract— The rise of neuromorphic applications has

    highlighted the remarkable potential of biologically-inspired

    systems. Despite significant advancements in audio and visual

    technologies, research directed towards tactile sensing has not

    been as extensive. We propose a neuromorphic tactile system

    for sensing and processing that presents promising results for

    edge devices and applications. In this study, a neuromorphic

    tactile sensor, two data encoding techniques, and a two-layer

    spiking neural network (SNN) deployed on the AKD1000

    Akida Neuromorphic System on Chip (NSoC) were used to

    demonstrate the system's capabilities. Results from

    experiments on the ST-MNIST dataset showed high accuracy,

    with the complement-coded variant achieving 93.1%,

    outperforming previous state-of-the-art models for this dataset.

    Additionally, an exploratory study showed that early

    classification was possible, with most samples requiring only

    38% of the available events to classify correctly, reducing the

    amount of data that needs to be processed. The low power

    consumption and high throughput of both SNN models, with

    an average dynamic power consumption of 6.37 mW and 7.76

    mW and an average throughput of 586 and 589 frames-per-

    second respectively, make the proposed system suitable for

    edge devices with limited power and processing resources.

    Overall, the proposed tactile sensing system presents a

    promising solution for edge applications that require high

    accuracy, low power consumption, and high throughput….

    This study suggests that the proposed early-classification

    pipeline holds the potential to significantly reduce

    classification latency for real-time systems, thus avoidingthe

    trade-off between sample capture time and accuracy in time-

    critical environments. For instance, in applications such as

    autonomous vehicle control, faster inferences could be

    critical, and making inferences during data capture could

    provide intermediary results before a final classification is

    made with the complete data sample. By reducing the latency

    associated with the data collection window, this system could

    enable real-world systems to make classifications with even

    incomplete data before making a final high-confidence

    classification.

    V.CONCLUSION

    The proposed neuromorphic system for tactile sensing

    and processing presents promising results, offering a solution

    for edge devices. In this study, two data encoding techniques

    were used in combination with a two-layer SNN deployed on

    the Akida NSoC. Results from experiments on the ST-

    MNIST dataset showed high accuracy, with the complement

    coded variant achieving 93.1%, outperforming previous

    state-of-the-art models for this dataset. Additionally, it was

    found that early classification was possible, as most samples

    could be correctly classified with just 38% of the available

    events, thus enabling real-time systems to reduce latency

    induced by data collection. The low power consumption and

    high throughput of both SNN models, with average dynamic

    power consumption of 6.37 mW and 7.76 mW, and average

    throughput of 586 and 589 frames-per-second respectively,

    make this system suitable for edge devices with limited

    power and processing resources. In conclusion, the proposed

    tactile sensing system presents a promising solution for edge

    applications, with high accuracy, low power consumption,

    and high throughput.”

    https://ieeexplore.ieee.org/abstract/document/10168592/


    I have highlighted in red a most significant feature of AKIDA technology.
    This characteristic is very human. We all when driving see things ahead
    of us and without waiting to be completely sure that it is a person in an Italian
    Suit complimented by a Gucci tie we make the decision that it is human
    and could present a hazard and we react by reducing speed, hovering over
    the brake pedal, changing lanes etc.

    In other words we do not require 100% of the data to start activating our response
    mechanism. For us as human drivers even a shadow of a human starting to appear
    can be enough data.

    AKIDA unlike Von Neuman compute which needs to have a whole image of the
    object to process, it can just like a human commence to process the data coming
    from the camera and reach correct classifications with only 38% of the incoming
    data.

    We all know that driving safely is about having sufficient time to react to whatever
    occurs because no matter what a ton of metal does not stop instantly it always
    takes some time no matter who or what is at the wheel and the more time who or
    what has the better the outcomes.

    The revolutionary nature of AKIDA technology continues to confound even before
    the original purpose of this paper comes into play which was as it does to prove
    out a method for AKIDA technology to bring touch to robotics in the same way that
    human skin does.

    My opinion only DYOR

    Fact Finder

 
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