Principles of Neuromorphic Computing

Parallel Processing

Neuromorphic computing inherently operates on a parallel structure. In this system, every neuron and synapse functions autonomously and simultaneously, working alongside others. This independence and simultaneous operation of neurons and synapses significantly boost the computational capacity of neuromorphic systems. By distributing tasks among many neurons and synapses, it avoids overburdening any single processing unit with too many tasks.

Unified Memory and Processing

As previously mentioned, in neuromorphic computing, memory and processing functions are unified within a single component. While neurons are generally viewed as processing units and synapses as storage units, in many neuromorphic systems, both neurons and synapses are responsible for data processing and storage tasks. This unification helps to overcome von Neumann bottlenecks. Additionally, combining memory and processing into one element also contributes to reducing power consumption.


Neuromorphic systems possess an intrinsic ability to scale. Enhancing these systems with new capabilities or functions merely involves adding more neurons and synapses. Moreover, upgrading neuromorphic systems can be straightforwardly achieved by programming additional virtual neurons and synapses. Such scalability is a crucial aspect, especially for ever-growing networks like artificial intelligence systems.

Event-Driven Computation

Neuromorphic systems utilize an event-driven computational approach. This means that these 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. Generally, such spikes are infrequent in the network's operation. The capacity of neuromorphic systems to remain inactive during periods of no workload significantly lowers their energy requirements.


Neuromorphic systems are characterized by their inherent stochastic nature. The autonomous functioning of neurons and synapses in these systems leads to a level of unpredictability. Predicting the exact outcome or state of any process in neuromorphic computing is complex, due to the myriad of interactions and variables at play. This randomness is not just a challenge but also a feature that closely mimics the probabilistic behavior of biological neural networks. The stochastic nature of neuromorphic systems is allowing them to handle uncertain and variable real-world data more effectively.

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