DeepSouth AI

An innovative system that utilizes neuromorphic computing to develop an AI application with multiple functions.

While incorporating traditional elements of programming, our system is fundamentally built on a range of advanced supervised and unsupervised training algorithms.

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Parallel Processing

Every neuron and synapse functions autonomously and simultaneously, working alongside others. By distributing tasks among many neurons and synapses, neuromorphic systems avoid overburdening any single processing unit with too many tasks.

Unified Memory and Processing

Memory and processing functions are unified within a single component, where both neurons and synapses are responsible for data processing and storage tasks. This unification helps to overcome von Neumann bottlenecks.

Scalability

Neuromorphic systems possess an intrinsic ability to scale. Upgrading neuromorphic systems can be straightforwardly achieved by programming additional virtual neurons and synapses.

Event-Driven Computation

Neuromorphic systems become active only when there is a need for computation and the necessary data is present. Neurons and synapses in these systems are engaged solely when there are spikes, or data events, to process.

Stochasticity

The autonomous functioning of neurons and synapses in neuromorphic systems leads to a level of unpredictability. This randomness is not just a challenge but also a feature that closely mimics the probabilistic behavior of biological neural networks.