Analog Computing: from Fundamentals to Applications

In this tutorial we briefly review the fundamentals of analog computing. Starting from the historical use of operational amplifiers to solve differential equations, we show how today the analog approach, in combination with novel devices and paradigms such as in-memory computing, can address the energy efficiency challenges of operations such as matrix-vector multiplication, matrix inversion, and the forward pass of a decision tree or attention block. These operations are crucial for machine learning applications, especially in energy-constrained contexts.