Neurogenesis-Advanced Neuro-Extreme Energy Efficiency (NA-NEEE) is an innovative bio-inspired framework designed for Spatio-Temporal neural processing, focusing on enhancing energy efficiency in 3D-integrated neuromorphic architectures. This engine employs advanced techniques such as structural plasticity, dendritic computation, and bit-level temporal sparsity to drive continual learning and optimize performance.

Overview

This repository presents a cutting-edge Spatio-Temporal Framework that transitions from conventional dense matrix-matrix multiplications to Sparse Asynchronous Information Communication. By leveraging neuromorphic principles, the architecture is tailored for high-efficiency neural computation within integrated hardware contexts.

Key Technical Components

• Dendritic Computation Engine: Mimics the temporal depth of neural processing using non-point neuron logic to handle rank-order signals effectively.
• Temporal Sparsity Enforcement: Implements a modular algorithm that promotes bit-level sparsity, thereby significantly lowering the energy expenditure per inference.
• Hippocampal Vault (Continual Learning): Features a stateful feedback mechanism that applies structural plasticity to combat catastrophic forgetting in dynamic environments.
• 3D DRAM Co-Design Logic: Optimized for monolithic integration, this component utilizes memory stacking techniques, enhancing capacity and reducing latency for synaptic updates.

Operational Principles

• Sparsity Encoding: Activations are substituted with a sparsity mask ensuring only crucial neural pathways are utilized.
• Rank Order Coding: This method distributes information significance across time-multiplexed significance levels, moving beyond simple rate coding.
• One-Shot Declarative Learning: Incorporates an associative memory module that facilitates the rapid acquisition of information without extensive backpropagation.

Usage

The modular architecture of this engine allows it to be seamlessly integrated with neuromorphic accelerators or emulated on standard DRAM-heavy interfaces. This design is particularly beneficial for benchmarking energy-per-synaptic-operation (J/SOP), highlighting its applicability in advanced neural processing tasks.