Lecture

In-memory Computing: Fundamentals and Trends

Description

This lecture explores the concept of in-memory computing (IMC) as a means to enhance energy efficiency in machine learning tasks. It delves into the transformation of memory accesses into analog/mixed-signal computations, leading to significant energy reduction. The talk covers IMC design principles, current trends, and future opportunities in deploying IMCs at scale. The instructor, Naresh Shanbhag, a prominent figure in electrical and computer engineering, has been actively involved in research on energy-efficient systems for machine learning and signal processing.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (38)
Quantum Signal Processing
Covers IQ demodulation of noisy quantum signals using FPGA and MATLAB.
IQ Demodulation: FPGA and MATLAB
Explores IQ demodulation using FPGA and MATLAB for noisy quantum signals.
Nanowire Sensor
Focuses on nanowire sensors interfacing in parallel for various applications and innovations.
LabVIEW: Display and DAQ
Covers LabVIEW programming for data acquisition and display, including LabVIEW sound and visualization.
Fourier Transform: Derivatives and Laplace Transform
Explores the Fourier transform properties with derivatives and introduces the Laplace transform for signal transformation and solving differential equations.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.