Department of Computer Science

Ashoka University is a private university located in Sonipat, Haryana, India, focusing on liberal arts and sciences. It was established in 2014.&nbsp;It is guided by an academic council of academics and scholars from around the world. The council sets the university&#39;s academic standard, advising on matters of curriculum, faculty hiring and research.&nbsp;

Ashoka University offers a full-time 3 (+ 1) year undergraduate programme. The University comprises of&nbsp;2500 plus students, with a mix of both national and international students.&nbsp; &nbsp;

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Ashoka University

In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012–2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m3 with a 5–15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.

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PLoS neglected tropical diseases

An assessment of remotely sensed environmental variables on Dengue epidemiology in Central India

Data transfer across DRAM channels accounts for nearly a quarter of the total energy consumption of DDR4 DRAMs. Modern applications with high bandwidth requirements further increase channel energy consumption. However, channel energy consumption is dependent on data being transferred. Pseudo Open Drain (POD) asymmetric termination, used in current DDR4 systems, consumes energy only when 1's are being transmitted over the channels. Many modern applications, including AI/ML ones are resilient to errors in data, and can work well with approximate data. This resilience can vary widely across and within applications, which provides a number of ways for exploiting these characteristics to save data transfer energy across the DRAM channel. However, all DRAM data encoding schemes have been targeted towards applications that require exact data and are not approximation resilient. In this paper, we propose Zero Aware Configurable Data Encoding by Skipping Transfer (ZAC-DEST), a data encoding scheme to reduce the energy consumption of DRAM channels, specifically targeted towards approximate computing and error resilient applications. ZAC-DEST exploits the similarity between recent data transfers across channels and information about error resilience behaviour of applications to reduce on-die termination and switching energy by reducing the number of 1's transmitted over the channels. ZAC-DEST also provides a number of knobs for trading off application's accuracy for energy savings, and vice versa, and can be applied to both training and inference. We apply ZAC-DEST to five machine learning applications. On average, across all applications and configurations, we observed a reduction of 40% in termination energy and 37% in switching energy as compared to the state of the art data encoding technique BD-Coder with an average output quality loss of 10%. We show that if both training and testing are done assuming the presence of ZAC-DEST, the output quality of the applications can be improved upto 9× as compared to when ZAC-DEST is only applied during testing leading to energy savings during training and inference with increased output quality.

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IEEE Transactions on Circuits and Systems I: Regular Papers

Zero Aware Configurable Data Encoding by Skipping Transfer for Error Resilient Applications

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this). © 2021 IEEE.

Proceedings - 2021 IEEE European Symposium on Security and Privacy, Euro S and P 2021

Poster: FLATEE: Federated learning across trusted execution environments

Today, almost all computer systems use IEEE-754 floating point to represent real numbers. Recently, posit was proposed as an alternative to IEEE-754 floating point as it has better accuracy and a larger dynamic range. The configurable nature of posit, with varying number of regime and exponent bits, has acted as a deterrent to its adoption. To overcome this shortcoming, we propose fixed-posit representation where the number of regime and exponent bits are fixed, and present the design of a fixed-posit multiplier. We evaluate the fixed-posit multiplier on error-resilient applications of AxBench and OpenBLAS benchmarks as well as neural networks. The proposed fixed-posit multiplier has 47%, 38.5%, 22% savings for power, area and delay respectively when compared to posit multipliers and up to 70%, 66%, 26% savings in power, area and delay respectively when compared to 32-bit IEEE-754 multiplier. These savings are accompanied with minimal output quality loss (1.2% average relative error) across OpenBLAS and AxBench workloads. Further, for neural networks like ResNet-18 on ImageNet we observe a negligible accuracy loss (0.12%) on using the fixed-posit multiplier. © 2021 IEEE.

IEEE Transactions on Circuits and Systems II: Express Briefs

Fixed-Posit: A Floating-Point Representation for Error-Resilient Applications

We present a novel radar-based system for real-time indoor positioning and detection of objects and human-bodies with low-quality, inexpensive sensors. Using modern deep learning methods, we avoid the use of expensive hardware and computationally-expensive signal processing methods for object detection.We train our model on mini-Doppler maps, collected via software defined radios. Crucially, our system is different from existing RF-based detection systems as it operates in a less crowded frequency range of 433 MHz, allowing us to use inexpensive off-the shelf hardware. Our system, based on the VGG-16 model, reports high-accuracy results on: (1) classification of different objects/materials (plastic, glass, metal); (2) detection and classification of multiple visually and materially similar objects and the human-body; and (3) Simple object detection at different distances between the transmitter Tx and the receiver Rx.WALDO, using low frequency radio waves, is able to handle occlusions and bad lighting environments. Our results demonstrate that Deep Learning methods can be combined with inexpensive, low-frequency radars to achieve high accuracy in real-time on various useful tasks. © 2021 IEEE.

IEEE National Radar Conference - Proceedings

WALDO Finds You using Machine Learning: Wireless Adaptive Location and Detection of Objects

Affirmative action in the form of reservations is a divisive and contentious topic of policy in India. In this paper, we aim to create a principled and data-driven model to design the reservations policy in India. We look at some arguments against current policy and try to resolve them. We use statistical modeling to create our new framework, RAMSES (Rigorous and Adaptive Measurement of Socio-Economic Status). RAMSES measures the multidimensional disadvantage faced by an individual as an "adjusted income", which attempts to calibrate the quantum of compensatory aid in the form of reservations for that individual to have a level playing field. We illustrate our model using a case study. © 2020 Owner/Author.

COMPASS 2020 - Proceedings of the 2020 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

An Alternative to India's Reservation Policy: A Unified Framework for Rigorous and Adaptive Measurement of Socio-Economic Status

In a large chip, an asynchronous Network-on-Chip (NoC) is a suitable candidate for establishing an interconnection network between varied components. Architectural level simulation is an accepted methodology for evaluating such systems. In this paper, we propose a fast and versatile Asynchronous Network-on-Chip (NoC) Simulator - ANSim, which brings down the simulation time by 25 x, compared to the state of the art simulators. It can model and analyze all synchronous, asynchronous and mixed synchronous-asynchronous system of cores connected through NoC. ANSim can model routers with different delays, routers with asynchronous arbitration, connected in a wide range of topologies. ANSim supports individual routers modeled to have varying timing constraints. Further, it supports synthetic and real-workloads and produces system-level latency, throughput, power, power-gating and arbitration reports. ANSim has been verified against RTL models of NoCs and other RTL verified simulators. An open-source synchronous NoC router, TNoC and its asynchronous derivative are used to demonstrate ANSim's usefulness and features.

Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors

ANSim: A Fast and Versatile Asynchronous Network-On-Chip Simulator

The average user has between 90–130 online accounts [17], and around 3 × 1011 passwords are in use this year [10]. Most people are terrible at remembering “random” passwords, so they reuse or create similar passwords using a combination of predictable words, numbers, and symbols [16]. Previous password-generation or management protocols have imposed so large a cognitive load that users have abandoned them in favor of insecure yet simpler methods (e.g., writing them down or reusing minor variants). We describe a range of candidate human-computable “hash” functions suitable for use as password generators - as long as the human (with minimal education assumptions) keeps a single, easily-memorizable ‘master’ secret - and rate them by various metrics, including effective security. These functions hash master-secrets with user accounts to produce sub-secrets that can be used as passwords; FR(s, w) ⟶ y, which takes a website w and produces a password y, parameterized by the master secret s, which may or may not be a string. We exploit the unique configuration R of each user’s associative and implicit memory (detailed in Sect. 2) to ensure that sources of randomness unique to each user are present in each F. An adversary cannot compute or verify FR efficiently since R is unique to each individual; in that sense, our hash function is similar to a physically unclonable function [37]. For the algorithms we propose, the user need only complete primitive operations such as addition, spatial navigation or searching. Critically, most of our methods are also accessible to neurodiverse, or cognitively or physically differently-abled persons. Given the nature of these functions, it is not possible to directly use traditional cryptographic methods for analysis; so, we use an array of approaches, mainly related to entropy, to illustrate and analyze the same. We draw on cognitive, neuroscientific, and cryptographic research to use these functions as improved password management and creation systems, and present results from a survey (n = 134 individuals, with each candidate performing 2 schemes) investigating real-world usage of these methods and how people currently come up with their passwords. We also survey 400 websites to collate current password advice. © Springer Nature Switzerland AG 2020.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Trenchcoat: Human-computable hashing algorithms for password generation

A single-sign-on (SSO) is an authentication system that allows a user to log in with a single identity and password to any of several related, yet independent, server applications. SSO solutions eliminate the need for users to repeatedly prove their identities to different applications and hold different credentials for each application. Token-based authentication is commonly used to enable a SSO experience on the web and on enterprise networks. A large body of work considers distributed token generation which can protect the long term keys against a subset of breached servers. A recent work (CCS'18) introduced the notion of Password-based Threshold Authentication (PbTA) with the goal of making password-based token generation for SSO secure against server breaches that could compromise both long-term keys and user credentials. They also introduced a generic framework called PASTA that can instantiate a PbTA system. The existing SSO systems built on distributed token generation techniques, including the PASTA framework, do not admit password-update functionality. In this work, we address this issue by proposing a password-update functionality into the PASTA framework. We call the modified framework PAS-TA-U. As a concrete application, we instantiate PAS-TA-U to implement in Python a distributed SSH key manager for enterprise networks (ESKM) that also admits a password-update functionality for its clients. Our experiments show that the overhead of protecting secrets and credentials against breaches in our system compared to a traditional single server setup is low (average 119 ms in a 10-out-of-10 server setting on Internet with 80 ms round trip latency).

PAS-TA-U: PASsword-Based Threshold Authentication with Password Update

The architecture of main memory has experienced a paradigm shift in recent years, with non volatile memory technologies (NVM) like Phase Change Memory (PCM) being incorporated into the hierarchy at the same level as DRAM. This transformation is being carried out by either splitting the memory address across two or more memory technologies, or using a faster technology with higher lifetimes, typically the DRAM, as a cache for the higher capacity, albeit slower main memory made up of a NVM. Design of such hybrid architectures remains an active area of research from the perspective of DRAM-as-a-cache design, since DRAM could quickly become the bottleneck, as cache lookups require multiple accesses for reading tag and data. In this paper, we augment the DRAM-as-a-cache model with a novel DRAM cache prefetcher that builds on state of the art Alloy Cache. The new DRAM cache architecture allows for prefetching data at both cacheline and page granularities from the NVM and as a result, provides upto a maximum of 2× performance improvement over a state of the art baseline.