Memory Systems

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Overview In this course, we first provide a comprehensive overview of memory systems, taking an approach that covers both fundamentals and recent research. We first introduce fundamental principles and ideas, covering DRAM and emerging memory technologies as well as many architectural concepts and ideas related to memory organization, memory control, processing-in-memory, and memory latency / energy / bandwidth / reliability / security / QoS. We discuss major challenges facing modern memory systems (and the computing platforms we currently design around the memory system) in the presence of greatly increasing demand for data and its fast analysis. We examine some promising research and design directions to overcome these challenges. On the research-related part of course (sprinkled across topical lectures), we discuss the following key research topics in detail, focusing on both open problems and potential solution directions: Fundamental issues in memory reliability and security and how to enable fundamentally secure, reliable, safe architectures Enabling data-centric and hence fundamentally energy-efficient architectures that are capable of performing computation near data Reducing both latency and energy consumption by tackling the fixed-latency/energy mindset Enabling emerging memory technologies Enabling predictable and QoS-aware memory systems Research challenges and opportunities in enabling emerging NVM (non-volatile memory) technologies Scaling NAND flash memory and SSDs (solid state drives) into the future

Design of Digital Circuits and Computer Architecture

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Overview The class provides an introduction to the design of digital circuitry. The class covers the basics of the technical foundations of gates. An introduction to hardware description languages and their use in the design process follows.

Graph Data Structure

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Graph definition with examples, important graph types and representation of graph using adjacency matrix and adjacency list, comparison between adjacency matrix and adjacency list.

Learn C programming

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How a computer processes the program written in a high level language has been explained in this video. Basic details from how a transistor is used to build logic gates and digital circuits has been discussed. Full adder design using nor gate, invention of integrated circuits and recent progresses are also explained. Development of assembly language code, what is an assembler, how a high level language code is converted to binary and is executed are a few other concepts discussed.

Introduction to parallel Programming in Open MP

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Overview This course focuses on the shared memory programming paradigm. It covers concepts & programming principles involved in developing scalable parallel applications. Assignments focus on writing scalable programs for multi-core architectures using OpenMP and C. This is an introductory course in shared memory parallel programming suitable for computer science as well as non-computer science students working on parallel/HPC applications and interested in parallel programming

Dynamic Behaviour Of Materials

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Introduction: Dynamic deformation and failure, Introduction to waves: Elastic waves; Types of elastic waves; Reflection, Refraction Interaction of waves, Plastic waves and shock etc...

Introduction to R Software

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Overview Any scientific task without the knowledge of software is difficult to imagine and complete in the current scenario. R is a free software that is capable of handling mathematical and statistical manipulations. It has its own programming language as well as built in functions to perform any specialized task. We intend to learn the basics of R software in this course.

Computational Continuum Mechanics

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Introduction - origins of nonlinearity, Mathematical Preliminaries -1: Tensors and tensor algebra, Mathematical Preliminaries -2: Linearization and directional derivative, Tensor analysis etc

Artificial Intelligence Search Methods for problem Solving

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Overview For an autonomous agent to behave in an intelligent manner it must be able to solve problems. This means it should be able to arrive at decisions that transform a given situation into a desired or goal situation. The agent should be able to imagine the consequence of its decisions to be able to identify the ones that work. In this first course on AI we study a wide variety of search methods that agents can employ for problem solving.