Shovan Maity

Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity et al.

Neuromorphic computing, inspired by the brain, promises extreme efficiency for certain classes of learning tasks, such as classification and pattern recognition. The performance and power consumption of neuromorphic computing depends heavily on the choice of the neuron architecture. Digital neurons (Dig-N) are conventionally known to be accurate and efficient at high speed, while suffering from high leakage currents from a large number of transistors in a large design. On the other hand, analog/mixed-signal neurons are prone to noise, variability and mismatch, but can lead to extremely low-power designs. In this work, we will analyze, compare and contrast existing neuron architectures with a proposed mixed-signal neuron (MS-N) in terms of performance, power and noise, thereby demonstrating the applicability of the proposed mixed-signal neuron for achieving extreme energy-efficiency in neuromorphic computing. The proposed MS-N is implemented in 65 nm CMOS technology and exhibits > 100X better energy-efficiency across all frequencies over two traditional digital neurons synthesized in the same technology node. We also demonstrate that the inherent error-resiliency of a fully connected or even convolutional neural network (CNN) can handle the noise as well as the manufacturing non-idealities of the MS-N up to certain degrees. Notably, a system-level implementation on MNIST datasets exhibits a worst-case increase in classification error by 2.1% when the integrated noise power in the bandwidth is ~ 0.1 uV2, along with +-3σ amount of variation and mismatch introduced in the transistor parameters for the proposed neuron with 8-bit precision.

Shovan Maity, Mingxuan He, Mayukh Nath et al.

Human Body Communication (HBC) has emerged as an alternative to radio wave communication for connecting low power, miniaturized wearable and implantable devices in, on and around the human body which uses the human body as the communication channel. Previous studies characterizing the human body channel has reported widely varying channel response much of which has been attributed to the variation in measurement setup. This calls for the development of a unifying bio physical model of HBC supported by in depth analysis and an understanding of the effect of excitation, termination modality on HBC measurements. This paper characterizes the human body channel up to 1MHz frequency to evaluate it as a medium for broadband communication. A lumped bio physical model of HBC is developed, supported by experimental validations that provides insight into some of the key discrepancies found in previous studies. Voltage loss measurements are carried out both with an oscilloscope and a miniaturized wearable prototype to capture the effects of non common ground. Results show that the channel loss is strongly dependent on the termination impedance at the receiver end, with up to 4dB variation in average loss for different termination in an oscilloscope and an additional 9 dB channel loss with wearable prototype compared to an oscilloscope measurement. The measured channel response with capacitive termination reduces low frequency loss and allows flat band transfer function down to 13 KHz, establishing the human body as a broadband communication channel. Analysis of the measured results and the simulation model shows that (1) high impedance (2) capacitive termination should be used at the receiver end for accurate voltage mode loss measurements of the HBC channel at low frequencies.

Shovan Maity, Debayan Das, Baibhab Chatterjee et al.

Human Body Communication (HBC) has recently emerged as an alternative to radio frequency transmission for connecting devices on and in the human body with order(s) of magnitude lower energy. The communication between these devices can give rise to different scenarios, which can be classified as wearable-wearable, wearable-machine, machine-machine interactions. In this paper, for the first time, the human body channel characteristics is measured for a wide range of such possible scenarios (14 vs. a few in previous literature) and classified according to the form-factor of the transmitter and receiver. The effect of excitation/termination configurations on the channel loss is also explored, which helps explain the previously unexplained wide variation in HBC Channel measurements. Measurement results show that wearable-wearable interaction has the maximum loss (upto -50 dB) followed by wearable-machine and machinemachine interaction (min loss of 0.5 dB), primarily due to the small ground size of the wearable devices. Among the excitation configurations, differential excitation is suitable for small channel length whereas single ended is better for longer channel.

Baibhab Chatterjee, Debayan Das, Shovan Maity et al.

Traditional authentication in radio-frequency (RF) systems enable secure data communication within a network through techniques such as digital signatures and hash-based message authentication codes (HMAC), which suffer from key recovery attacks. State-of-the-art IoT networks such as Nest also use Open Authentication (OAuth 2.0) protocols that are vulnerable to cross-site-recovery forgery (CSRF), which shows that these techniques may not prevent an adversary from copying or modeling the secret IDs or encryption keys using invasive, side channel, learning or software attacks. Physical unclonable functions (PUF), on the other hand, can exploit manufacturing process variations to uniquely identify silicon chips which makes a PUF-based system extremely robust and secure at low cost, as it is practically impossible to replicate the same silicon characteristics across dies. Taking inspiration from human communication, which utilizes inherent variations in the voice signatures to identify a certain speaker, we present RF- PUF: a deep neural network-based framework that allows real-time authentication of wireless nodes, using the effects of inherent process variation on RF properties of the wireless transmitters (Tx), detected through in-situ machine learning at the receiver (Rx) end. The proposed method utilizes the already-existing asymmetric RF communication framework and does not require any additional circuitry for PUF generation or feature extraction. Simulation results involving the process variations in a standard 65 nm technology node, and features such as LO offset and I-Q imbalance detected with a neural network having 50 neurons in the hidden layer indicate that the framework can distinguish up to 4800 transmitters with an accuracy of 99.9% (~ 99% for 10,000 transmitters) under varying channel conditions, and without the need for traditional preambles.

Debayan Das, Shovan Maity, Baibhab Chatterjee et al.

In this emerging data-driven world, secure and ubiquitous authentication mechanisms are necessary prior to any confidential information delivery. Biometric authentication has been widely adopted as it provides a unique and non-transferable solution for user authentication. In this article, the authors envision the need for an in-field, remote and on-demand authentication system for a highly mobile and tactical environment, such as critical information delivery to soldiers in a battlefield. Fingerprint-based in-field biometric authentication combined with the conventional password-based techniques would ensure strong security of critical information delivery. The proposed in-field fingerprint authentication system involves: (i) wearable fingerprint sensor, (ii) template extraction (TE) algorithm, (iii) data encryption, (iv) on-body and long-range communications, all of which are subject to energy constraints due to the requirement of small form-factor wearable devices. This paper explores the design space and provides an optimized solution for resource allocation to enable energy-efficient in-field fingerprint-based authentication. Using Human Body Communication (HBC) for the on-body data transfer along with the analytics (TE algorithm) on the hub allows for the maximum lifetime of the energy-sparse sensor. A custom-built hardware prototype using COTS components demonstrates the feasibility of the in-field fingerprint authentication framework.

Baibhab Chatterjee, Priyadarshini Panda, Shovan Maity et al.

This work presents the design and analysis of a mixed-signal neuron (MS-N) for convolutional neural networks (CNN) and compares its performance with a digital neuron (Dig-N) in terms of operating frequency, power and noise. The circuit-level implementation of the MS-N in 65 nm CMOS technology exhibits 2-3 orders of magnitude better energy-efficiency over Dig-N for neuromorphic computing applications - especially at low frequencies due to the high leakage currents from many transistors in Dig-N. The inherent error-resiliency of CNN is exploited to handle the thermal and flicker noise of MS-N. A system-level analysis using a cohesive circuit-algorithmic framework on MNIST and CIFAR-10 datasets demonstrate an increase of 3% in worst-case classification error for MNIST when the integrated noise power in the bandwidth is ~ 1 μV2.

Debayan Das, Shovan Maity, Saad Bin Nasir et al.

With the advancement of technology in the last few decades, leading to the widespread availability of miniaturized sensors and internet-connected things (IoT), security of electronic devices has become a top priority. Side-channel attack (SCA) is one of the prominent methods to break the security of an encryption system by exploiting the information leaked from the physical devices. Correlational power attack (CPA) is an efficient power side-channel attack technique, which analyses the correlation between the estimated and measured supply current traces to extract the secret key. The existing countermeasures to the power attacks are mainly based on reducing the SNR of the leaked data, or introducing large overhead using techniques like power balancing. This paper presents an attenuated signature AES (AS-AES), which resists SCA with minimal noise current overhead. AS-AES uses a shunt low-drop-out (LDO) regulator to suppress the AES current signature by 400x in the supply current traces. The shunt LDO has been fabricated and validated in 130 nm CMOS technology. System-level implementation of the AS-AES along with noise injection, shows that the system remains secure even after 50K encryptions, with 10x reduction in power overhead compared to that of noise addition alone.

Victor Seal, Arnab Raha, Shovan Maity et al.

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.