PULP-HD: Accelerating Brain-Inspired High-Dimensional Computing on a Parallel Ultra-Low Power Platform

Computing with high-dimensional (HD) vectors, also referred to as $\textit{hypervectors}$, is a brain-inspired alternative to computing with scalars. Key properties of HD computing include a well-defined set of arithmetic operations on hypervectors, generality, scalability, robustness, fast learning, and ubiquitous parallel operations. HD computing is about manipulating and comparing large patterns-binary hypervectors with 10,000 dimensions-making its efficient realization on minimalistic ultra-low-power platforms challenging. This paper describes HD computing's acceleration and its optimization of memory accesses and operations on a silicon prototype of the PULPv3 4-core platform (1.5mm$^2$, 2mW), surpassing the state-of-the-art classification accuracy (on average 92.4%) with simultaneous 3.7$\times$ end-to-end speed-up and 2$\times$ energy saving compared to its single-core execution. We further explore the scalability of our accelerator by increasing the number of inputs and classification window on a new generation of the PULP architecture featuring bit-manipulation instruction extensions and larger number of 8 cores. These together enable a near ideal speed-up of 18.4$\times$ compared to the single-core PULPv3

An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier

EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial

The proinflammatory cytokine interleukin 18 regulates feeding by acting on the bed nucleus of the stria terminalis

The proinflammatory cytokine IL-18 has central anorexigenic effects and was proposed to contribute to loss of appetite observed during sickness. Here we tested in the mouse the hypothesis that IL-18 can decrease food intake by acting on neurons of the bed nucleus of the stria terminalis (BST), a component of extended amygdala recently shown to influence feeding via its projections to the lateral hypothalamus (LH). We found that both subunits of the heterodimeric IL-18 receptor are highly expressed in the BST and that local injection of recombinant IL-18 (50 ng/ml) significantly reduced c-fos activation and food intake for at least 6 h. Electrophysiological experiments performed in BST brain slices demonstrated that IL-18 strongly reduces the excitatory input on BST neurons through a presynaptic mechanism. The effects of IL-18 are cell-specific and were observed in Type III but not in Type I/II neurons. Interestingly, IL-18-sensitve Type III neurons were recorded in the juxtacapsular BST, a region that contains BST-LH projecting neurons. Reducing the excitatory input on Type III GABAergic neurons, IL-18 can increase the firing of glutamatergic LH neurons through a disinhibitory mechanism. Imbalance between excitatory and inhibitory activity in the LH can induce changes in food intake. Effects of IL-18 were mediated by the IL-18R because they were absent in neurons from animals null for IL-18R\u3b1 (Il18ra-/-), which lack functional IL-18 receptors. In conclusion, our data show that IL-18 may inhibit feeding by inhibiting the activity of BST Type III GABAergic neurons

Advanced Interfaces for HMI in Hand Gesture Recognition

The present thesis investigates techniques and technologies for high quality Human Machine Interfaces (HMI) in biomedical applications. Starting from a literature review and considering market SoA in this field, the thesis explores advanced sensor interfaces, wearable computing and machine learning techniques for embedded resource-constrained systems. The research starts from the design and implementation of a real-time control system for a multifinger hand prosthesis based on pattern recognition algorithms. This system is capable to control an artificial hand using a natural gesture interface, considering the challenges related to the trade-off between responsiveness, accuracy and light computation. Furthermore, the thesis addresses the challenges related to the design of a scalable and versatile system for gesture recognition with the integration of a novel sensor interface for wearable medical and consumer application

Compressed sensing based seizure detection for an ultra low power multi-core architecture

Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency

First observation of quantum interference in the process phi -> KS KL ->pi+pi-pi+pi-: a test of quantum mechanics and CPT symmetry

We present the first observation of quantum interference in the process phi -> KS KL ->pi+pi-pi+pi-. This analysis is based on data collected with the KLOE detector at the e^+e^- collider DAFNE in 2001--2002 for an integrated luminosity of about 380pb^-1. Fits to the distribution of Delta t, the difference between the two kaon decay times, allow tests of the validity of quantum mechanics and CPT symmetry. No deviations from the expectations of quantum mechanics and CPT symmetry have been observed. New or improved limits on various decoherence and CPT violation parameters have been obtainedComment: submitted to Physics Letter B one number changed old:gamma=(1.1+2.9-2.4)10^-21 GeV new:(1.3+2.8-2.4)10^-21GeV corrected typo

BioGAP: a 10-Core FP-capable Ultra-Low Power IoT Processor, with Medical-Grade AFE and BLE Connectivity for Wearable Biosignal Processing

Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and machine Learning (ML) near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multi-precision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAPs form factor is 16x21x14 mm$^3$ and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless Bluetooth Low Energy (BLE) capable SoC, a power management circuit, and an accelerometer; and a shield including an analog front-end (AFE) for ExG acquisition. Finally, the system also includes a flexibly placeable photoplethysmogram (PPG) PCB with a size of 9x7x3 mm$^3$ and a rechargeable battery ($\phi$ 12x5 mm$^2$). We demonstrate BioGAP on a Steady State Visually Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) application. We achieve 3.6 uJ/sample in streaming and 2.2 uJ/sample in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7 Mflops/s/mW with wireless bandwidth reduction of 97%, within a power budget of just 18.2 mW allowing for an operation time of 15 h.Comment: 7 pages, 9 figures, 1 table, accepted for IEEE COINS 202