Electrical Engineering: Designing Circuits as Neural Networks

## Project Description The project involves designing circuits that function as neural networks. These circuits emulate the behavior of biological neurons and synapses, performing essential neural network operations such as weighted sum calculations, activation functions, and signal propagation. The training of these circuits can be conducted either directly on the hardware or through comprehensive software simulations using SPICE tools. ## Technologies Used - **Python**: For interfacing with simulation tools and neural network frameworks. - **SPICE Simulation Software**: Tools like PySpice and NgSpice for modeling and simulating circuit behavior. - **Neural Network Frameworks**: TensorFlow and PyTorch for virtual simulation and training of neural networks. ## Challenges Faced ### 1. **Emulating Neurons and Synapses** - **Challenge**: Accurately replicating the complex functionality of biological neurons and synapses in a circuit. - **Solution**: Splitting the design into modular components, coming up with tests to validate the behavior, and refining the circuit iteratively. ### 2. **In-circuit Training** - **Challenge**: Developing methods to train neural networks directly on hardware. - **Solution**: Replicate state-of-the-art training techniques and adapt them for in-circuit training. ### 3. **Software Simulation** - **Challenge**: Ensuring that software simulations accurately represent hardware behavior. - **Solution**: Using circuit simulation software like SPICE, and interfacing with Python libraries such as PySpice and NgSpice to optimize designs before hardware implementation. ## Domain Knowledge Requirements In addition to AI development expertise, the project required specialized knowledge in the following areas: - **Neuroscience**: Basic understanding of the functionality of biological neurons and synapses to accurately emulate their behavior in circuits. - **Circuit Design and Optimization**: Electrical engineering knowledge to design efficient circuits and optimize their performance for neural network operations. - **Simulation and Modeling**: Proficiency in using simulation tools like SPICE and Python libraries such as PySpice and NgSpice for accurate modeling and optimization.