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== Overview ==
 
== Overview ==
 
Akida is a neuromorphic [[system on a chip]] designed for a wide range of markets from edge inference and training with a sub-1W power to high-performance data center applications. The architecture consists of three major parts: sensor interfaces, the conversion complex, and the neuron fabric. Depending on the application (e.g., edge vs data center) data may either be collected at the device (e.g. lidar, visual and audio) or brought via one of the standard data interfaces (e.g., [[PCIe]]). Any data sent to the Akida SoC requires being converted into spikes to be useful. Akida incorporates a conversion complex with a set of specialized conversion units for handling digital, analog, vision, sound and other data types to spikes.
 
Akida is a neuromorphic [[system on a chip]] designed for a wide range of markets from edge inference and training with a sub-1W power to high-performance data center applications. The architecture consists of three major parts: sensor interfaces, the conversion complex, and the neuron fabric. Depending on the application (e.g., edge vs data center) data may either be collected at the device (e.g. lidar, visual and audio) or brought via one of the standard data interfaces (e.g., [[PCIe]]). Any data sent to the Akida SoC requires being converted into spikes to be useful. Akida incorporates a conversion complex with a set of specialized conversion units for handling digital, analog, vision, sound and other data types to spikes.
 
=== Fabric ===
 
Akida incorporates a Neuron fabric along with a processor complex used for system and data management as well as training and inference control. The chip efficiency comes from their ability to take advantage of sparsity with neurons only firing once a programmable threshold is exceeded. NNs are feed-forward. Neurons learn through selective reinforcement or inhibition of synapses. Sensory data such as images are converted into spikes. The Akida NSoC has neuron fabric comprised of 1.2 million neurons and 10 billion synapses. For training, both supervised and unsupervised modes are supported. In the supervised mode, initial layers of the network are trained autonomously with the labels being applied to the final fully-connected layer. This makes it possible for the networks to function as classification networks. Unsupervised learning from unlabeled data as well as label classification is possible.
 
  
 
=== Scalability ===
 
=== Scalability ===

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codenameAkida +
designerBrainChip +
first launchedSeptember 10, 2018 +
full page namebrainchip/akida +
instance ofmicroarchitecture +
manufacturerTSMC +
nameAkida +