This course aims to explore computational memory as a pathway to non-von Neumann computing. The learning outcomes include understanding the challenges in traditional von Neumann computing, exploring in-memory computing beyond von Neumann architecture, and learning about the constituent elements and capabilities of computational memory. The course teaches skills such as logic design using resistive memory devices, stateful logic, bulk bitwise operations, and matrix-vector multiplication using PCM devices. The teaching method involves theoretical explanations, practical examples, and experimental results. The intended audience for this course includes students, researchers, and professionals interested in advanced computing architectures and memory technologies.
Computational Memory - A Stepping-Stone to Non-Von Neumann Computing?
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Overview
Syllabus
Introduction.
IBM Research - Zurich.
The Al revolution.
The computing challenge.
Advances in von Neumann computing Storage class memory.
Beyond von Neumann: In-memory computing.
Constituent elements of computational memory.
Multi-level storage capability.
Rich dynamic behavior.
Logic design using resistive memory devices.
Stateful logic.
Bulk bitwise operations.
Matrix-vector multiplication.
Storing a matrix element in a PCM device.
Scalar multiplication using PCM devices.
Application: Compressed sensing and recovery.
Compressed sensing using computational memory.
Compressive imaging: Experimental results.
Crystallization dynamics in PCM.
Example 1: Finding the factors of numbers.
Finding the factors of numbers in parallel.
Example 2: Unsupervised learning of correlations.
Realization using computational memory.
Experimental results (1 Million PCM devices) Device conductance.
Comparative study.
The challenge of imprecision!.
Application 1: Mixed-precision linear solver.
Mixed-precision linear solver: Experimental results.
Application to gene interaction networks.
Application 2: Training deep neural networks.
Taught by
Stanford Online
