Quantum Circuits for Toom-Cook Multiplication

In this paper, we report efficient quantum circuits for integer multiplication using Toom-Cook algorithm. By analysing the recursive tree structure of the algorithm, we obtained a bound on the count of Toffoli gates and qubits. These bounds are further improved by employing reversible pebble games through uncomputing the intermediate results. The asymptotic bounds for different performance metrics of the proposed quantum circuit are superior to the prior implementations of multiplier circuits using schoolbook and Karatsuba algorithms

Architectures and automation for beyond-CMOS technologies

As the CMOS technology scaling is facing fundamental issues, there has been a rise of novel beyond-CMOS technologies. Among those, several compute-in-memory technologies and quantum computing prominently stands out. For both of these classes of technologies, there is an urgent need of robust and efficient Electronic Design Automation (EDA) flows. In this research, several EDA challenges for the emerging technologies are addressed, including technology mapping, and logic synthesis for in-memory computing and quantum computing. Furthermore, a novel, native implementation of fuzzy logic on ReRAM devices has been practically demonstrated.Doctor of Philosoph

Ensemble Classifier based approach for Code-Mixed Cross-Script Question Classification [Team IINTU]

ABSTRACT With an increasing popularity of social-media, people post updates that aid other users in finding answers to their questions. Most of the user-generated data on social-media are in code-mixed or multi-script form, where the words are represented phonetically in a non-native script. We address the problem of Question-Classfication on social-media data. We propose an ensemble classifier based approach towards question classification when the questions are written in mixedscript, specifically, the Roman script for the Bengali language. We separately train Random Forests, One-Vs-Rest and k-NN classifiers and then build an ensemble classifier that combines the best from the three worlds. We achieve an accuracy of 82% approximately, suggesting that the method works well in the task

Ensemble classifier based approach for code-mixed cross-script question classification

With an increasing popularity of social-media, people post updates that aid other users in finding answers to their questions. Most of the user-generated data on social-media are in code-mixed or multi-script form, where the words are represented phonetically in a non-native script. We address the problem of Question-Classfication on social-media data. We propose an ensemble classifier based approach towards question classification when the questions are written in mixedscript, specifically, the Roman script for the Bengali language. We separately train Random Forests, One-Vs-Rest and k-NN classifiers and then build an ensemble classifier that combines the best from the three worlds. We achieve an accuracy of 82% approximately, suggesting that the method works well in the task.Published versio

Crossbar-constrained technology mapping for ReRAM based in-memory computing

In-memory computing has gained significant attention due to the potential for dramatic improvement in speed and energy. Redox-based resistive RAMs (ReRAMs), capable of non-volatile storage and logic operations simultaneously have been used for logic-in-memory computing approaches. To this effect, we propose ReRAM based VLIW Architecture for in-Memory comPuting (ReVAMP), supported by a detailed device-accurate simulation setup with peripheral circuitry. We present theoretical bounds on the minimum area required for in-memory computation of arbitrary Boolean functions specified using structural representation (And-Inverter Graph and Majority-Inverter Graph) and two-level representation (Exclusive-Sum-of-Product). To support the ReVAMP architecture, we present two technology mapping flows that fully exploit the bit-level parallelism offered by the execution of logic using ReRAM crossbar array. The area-constrained mapping (ArC) generates feasible mapping for a variety of crossbar dimensions while the delay-constrained mapping (DeC) focuses primarily on minimizing the latency of mapping. We evaluate the proposed mappings against two state-of-the-art technology in-memory computing architectures, PLiM and MAGIC along with their automation flows (SIMPLE and COMPACT). ArC and DeC outperform state-of-the-art PLiM architecture by 1.46×1.46× and 4.3×4.3× on average in latency. ArC offers significantly lower area (on average 25.27×25.27× and 6.57×6.57×), while improving the area-delay product by 1.37×1.37× and 1.12×1.12× against two mapping approaches for MAGIC respectively. In contrast, DeC achieves average area (1.45×1.45× and 3.06×3.06×) and area-delay product (1.12×1.12× and 6.36×6.36×) improvements over the mapping approaches for MAGIC architecture respectively. The proposed mapping techniques allow a variety of runtime efficiency trade-offs