Showing papers by "Nagarajan Kandasamy published in 2023"

Hardware-Software Co-Design for On-Chip Learning in AI Systems

Abstract: Spike-based convolutional neural networks (CNNs) are empowered with on-chip learning in their convolution layers, enabling the layer to learn to detect features by combining those extracted in the previous layer. We propose ECHELON, a generalized design template for a tile-based neuromorphic hardware with on-chip learning capabilities. Each tile in ECHELON consists of a neural processing units (NPU) to implement convolution and dense layers of a CNN model, an on-chip learning unit (OLU) to facilitate spike-timing dependent plasticity (STDP) in the convolution layer, and a special function unit (SFU) to implement other CNN functions such as pooling, concatenation, and residual computation. These tile resources are interconnected using a shared bus, which is segmented and configured via the software to facilitate parallel communication inside the tile. Tiles are themselves interconnected using a classical Network-on-Chip (NoC) interconnect. We propose a system software to map CNN models to ECHELON, maximizing the performance. We integrate the hardware design and software optimization within a co-design loop to obtain the hardware and software architectures for a target CNN, satisfying both performance and resource constraints. In this preliminary work, we show the implementation of a tile on a FPGA and some early evaluations. Using 8 STDP-enabled CNN models, we show the potential of our co-design methodology to optimize hardware resources.

Online Performance Monitoring of Neuromorphic Computing Systems

Abstract: Neuromorphic computation is based on spike trains in which the location and frequency of spikes occurring within the network guide the execution. This paper develops a frame-work to monitor the correctness of a neuromorphic program’s execution using model-based redundancy in which a software-based monitor compares discrepancies between the behavior of neurons mapped to hardware and that predicted by a corresponding mathematical model in real time. Our approach reduces the hardware overhead needed to support the monitoring infrastructure and minimizes intrusion on the executing application. Fault-injection experiments utilizing CARLSim, a high-fidelity SNN simulator, show that the framework achieves high fault coverage using parsimonious models which can operate with low computational overhead in real time.

Dove: Shoulder-Based Opioid Overdose Detection and Reversal Device

Abstract: Naloxone is a life-saving drug capable of reversing a fatal opioid overdose. Although this drug has existed for over 50 years, opioid overdose-related deaths have consistently risen and surpassed 120,000 globally in 2021. Opioids induce respiratory depression by activating $\mu\text{-opioid}$ receptors at specific sites in the central nervous system. This results in overdose deaths caused by slow and shallow breathing, also known as opioid-induced respiratory depression. 1.6 million individuals suffer from opioid use disorder annually, making them at high risk of overdose, primarily due to the increasing prevalence of Fentanyl. Over 52% of these deaths occur when the individual is alone. Immediate response to an overdose by delivering naloxone can save the individual's life. To solve this problem, we developed a closed-loop sensor-driven auto-injector that can determine a fatal overdose and inject naloxone. 76% of this population is willing to wear such a device on the shoulder, a canonical injection site. This paper presents the DOVE, a shoulder-based opioid overdose detection and reversal device. It noninvasively measures the subject's motion state and changes in blood oxygen levels (SpO2) along with the respiration state. These biomarkers are measured from the shoulder using an optical sensor and accelerometer to determine if a fatal overdose occurred. We evaluated our DOVE device against an FDA-cleared commercial pulse oximeter by inducing apneic events as they have very similar SpO2 trends to an overdose. Results show that SpO2 can be measured on the shoulder across different skin tones with an accuracy of 96.8% and a high Pearson correlation of 0.766 (p < 0.0001).