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Computation in Neuromorphic Analog VLSI Systems (CNS)
Computation in Neuromorphic analog VLSI Systems (CNS)
Last Updated: 2026-02-05 15:06:58
Abstract
This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, motion circuits) and an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions.
Objective
Understanding of the characteristics of neuromorphic circuit elements and their interaction in parallel networks.
Content
Neuromorphic analog circuits are inspired by the structure, function and plasticity of biological neurons and neural networks. Their computational primitives are based on electronic and optical properties of the physical structures in and on the semiconductor substrate. Neuromorphic algorithms typically rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally at each processing stage within the individual computational elements. Transistors are primarily operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. The high parallelism and connectivity of neuromorphic circuits permit structures with massive feedback without iterative methods and convergence problems and real-time processing networks for high-dimensional signals (e.g. images). Application domains of neuromorphic circuits include detailed real-time simulations of biological neurons and neural networks and the development of autonomous systems in robotics, vehicle guidance, and traffic control. This course covers elementary devices in CMOS and BiCMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, multipliers, power-law circuits, resistive networks, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina, motion circuits) and an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to entire systems.
Resources
Literature
S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications.
General Information
- Language
- English
- Levels
- DS , BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 20 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture |
Computation in Neuromorphic analog VLSI Systems (CNS)
gemeinsam mit Uni Zürich
|
|
2 h weekly |
| exercise |
Computation in Neuromorphic analog VLSI Systems (CNS)
Uni Irchel Y17 M05
gemeinsam mit Uni Zürich
|
|
3 h weekly |