Hello everyone,

I am a second-year Control and Automation Engineering student. Since my childhood, I’ve been deeply involved in the "maker" community, working with microcontrollers and basic electronics. Currently, I am working on Deep Learning projects, but my primary interests remain rooted in Control Theory and Analog Electronics.

Recently, I’ve been researching Analog Neural Networks (ANNs). I am currently teaching myself how to design a neuron’s logic using analog circuitry (op-amps, resistors, etc.). However, I have some questions regarding the career path and the industry's direction in this field:

1. Relevance: Is Analog Neural Network research considered a "legacy" topic, or is it gaining modern traction? With the rise of Neuromorphic Computing and In-Memory Computing, does focusing on analog implementations of AI provide a viable career path?
2. Control Theory Integration: Can I bridge the gap between Deep Learning and Control Theory through analog hardware?
3. Feasibility: For someone at my level, is diving deep into analog AI hardware a sound investment of time, or should I stick to the digital/software side of ML?

I would love to hear from anyone with experience in VLSI, Neuromorphic systems, or advanced Control Systems. As a sophomore, I am eager to align my self-study with the future of the industry. Additionally, as a sophomore student, I am open to any career or project advice. What specific sub-topics or emerging technologies should I focus on to stay ahead in the next 5-10 years?

Thank you in advance for your guidance!

 

Hello everyone,

I am a second-year Control and Automation Engineering student. Since my childhood, I’ve been deeply involved in the "maker" community, working with microcontrollers and basic electronics. Currently, I am working on Deep Learning projects, but my primary interests remain rooted in Control Theory and Analog Electronics.

Recently, I’ve been researching Analog Neural Networks (ANNs). I am currently teaching myself how to design a neuron’s logic using analog circuitry (op-amps, resistors, etc.). However, I have some questions regarding the career path and the industry's direction in this field:

1. Relevance: Is Analog Neural Network research considered a "legacy" topic, or is it gaining modern traction? With the rise of Neuromorphic Computing and In-Memory Computing, does focusing on analog implementations of AI provide a viable career path?
2. Control Theory Integration: Can I bridge the gap between Deep Learning and Control Theory through analog hardware?
3. Feasibility: For someone at my level, is diving deep into analog AI hardware a sound investment of time, or should I stick to the digital/software side of ML?

I would love to hear from anyone with experience in VLSI, Neuromorphic systems, or advanced Control Systems. As a sophomore, I am eager to align my self-study with the future of the industry. Additionally, as a sophomore student, I am open to any career or project advice. What specific sub-topics or emerging technologies should I focus on to stay ahead in the next 5-10 years?

Thank you in advance for your guidance!

Analog neural networks are mostly educational today. Pure op-amp neurons are not industry relevant.
However, mixed-signal AI hardware is growing fast. Control theory and machine learning connect strongly mathematically. State-space models relate to recurrent neural networks. Analog circuits naturally solve differential equations. But digital ML dominates real-world deployment. Best career path mixes control, embedded systems, and ML. Add analog electronics as a differentiator skill.
Ref:
https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.959626/full
https://www.sciencedirect.com/science/article/pii/S0141933125001073
https://www.mdpi.com/2079-9292/14/16/3159