Abstract: A neural inference chip is provided, including at least one neural inference core. The at least one neural inference core is adapted to apply a plurality of synaptic weights to a plurality of input activations to produce a plurality of intermediate outputs. The at least one neural inference core comprises a plurality of activation units configured to receive the plurality of intermediate outputs and produce a plurality of activations. Each of the plurality of activation units is configured to apply a configurable activation function to its input. The configurable activation function has at least a re-ranging term and a scaling term, the re-ranging term determining the range of the activations and the scaling term determining the scale of the activations. Each of the plurality of activations units is configured to obtain the re-ranging term and the scaling term from one or more look up tables.

Abstract: Neural network accelerator output ranking is provided. In various embodiments, a system comprises a data memory; a memory controller configured to access the data memory; a plurality of comparators configured in a tree; a register; and a two-way comparator. The memory controller is configured to provide a first plurality of values from the data memory to the comparator tree. The comparator tree is configured to perform a plurality of concurrent pairwise comparisons of the first plurality of values to arrive at a first greatest value of the first plurality of values. The two-way comparator is configured to output the greater of the greatest value from the comparator tree and a stored value from the register. The register is configured to store the output of the two-way comparator.

Abstract: Embodiments of the invention relate to canonical spiking neurons for spatiotemporal associative memory. An aspect of the invention provides a spatiotemporal associative memory including a plurality of electronic neurons having a layered neural net relationship with directional synaptic connectivity. The plurality of electronic neurons configured to detect the presence of a spatiotemporal pattern in a real-time data stream, and extract the spatiotemporal pattern. The plurality of electronic neurons are further configured to, based on learning rules, store the spatiotemporal pattern in the plurality of electronic neurons, and upon being presented with a version of the spatiotemporal pattern, retrieve the stored spatiotemporal pattern.