Oindrila Chatterjee

Oindrila Chatterjee, Shantanu Chakrabartty

Sonification, or encoding information in meaningful audio signatures, has several advantages in augmenting or replacing traditional visualization methods for human-in-the-loop decision-making. Standard sonification methods reported in the literature involve either (i) using only a subset of the variables, or (ii) first solving a learning task on the data and then mapping the output to an audio waveform, which is utilized by the end-user to make a decision. This paper presents a novel framework for sonifying high-dimensional data using a complex growth transform dynamical system model where both the learning (or, more generally, optimization) and the sonification processes are integrated together. Our algorithm takes as input the data and optimization parameters underlying the learning or prediction task and combines it with the psychoacoustic parameters defined by the user. As a result, the proposed framework outputs binaural audio signatures that not only encode some statistical properties of the high-dimensional data but also reveal the underlying complexity of the optimization/learning process. Along with extensive experiments using synthetic datasets, we demonstrate the framework on sonifying Electro-encephalogram (EEG) data with the potential for detecting epileptic seizures in pediatric patients.

Oindrila Chatterjee, Shantanu Chakrabartty

Traditional energy-based learning models associate a single energy metric to each configuration of variables involved in the underlying optimization process. Such models associate the lowest energy state to the optimal configuration of variables under consideration, and are thus inherently dissipative. In this paper we propose an energy-efficient learning framework that exploits structural and functional similarities between a machine learning network and a general electrical network satisfying the Tellegen's theorem. In contrast to the standard energy-based models, the proposed formulation associates two energy components, namely, active and reactive energy to the network. This ensures that the network's active-power is dissipated only during the process of learning, whereas the reactive-power is maintained to be zero at all times. As a result, in steady-state, the learned parameters are stored and self-sustained by electrical resonance determined by the network's nodal inductances and capacitances. Based on this approach, this paper introduces three novel concepts: (a) A learning framework where the network's active-power dissipation is used as a regularization for a learning objective function that is subjected to zero total reactive-power constraint; (b) A dynamical system based on complex-domain, continuous-time growth transforms which optimizes the learning objective function and drives the network towards electrical resonance under steady-state operation; and (c) An annealing procedure that controls the trade-off between active-power dissipation and the speed of convergence. As a representative example, we show how the proposed framework can be used for designing resonant support vector machines (SVMs), where we show that the support-vectors correspond to an LC network with self-sustained oscillations.

P. Kumar, A. R. Nair, O. Chatterjee et al.

This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes the framework scalable and enables its implementation for low-power, high-density and memory constrained embedded application. An efficient hardware implementation of the same is also discussed, which utilizes novel low power memtransistor based cross-bar architecture, and is robust to device mismatch and randomness. We used memtransistor measurement data, and showed that the designed SVMs can achieve classification accuracy comparable to traditional SVMs on both synthetic and real-world benchmark datasets. This framework would be beneficial for design of SVM based wake-up systems for internet of things (IoTs) and edge devices where memtransistors can be used to optimize system's energy-efficiency and perform in-memory matrix-vector multiplication (MVM).

Oindrila Chatterjee, Shantanu Chakrabartty

In this paper, we show that different types of evolutionary game dynamics are, in principle, special cases of a dynamical system model based on our previously reported framework of generalized growth transforms. The framework shows that different dynamics arise as a result of minimizing a population energy such that the population as a whole evolves to reach the most stable state. By introducing a population dependent time-constant in the generalized growth transform model, the proposed framework can be used to explain a vast repertoire of evolutionary dynamics, including some novel forms of game dynamics with non-linear payoffs.