Abstract: Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a universal substrate of adaptation. One embodiment comprises a neurosynaptic device including a memory device that maintains neuron attributes for multiple neurons. The module further includes multiple bit maps that maintain incoming firing events for different periods of delay and a multi-way processor. The processor includes a memory array that maintains a plurality of synaptic weights. The processor integrates incoming firing events in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes and the synaptic weights maintained.

Abstract: An information processing system, which includes a control system and an artificial neural network, is disclosed. The artificial neural network includes a group of neurons and a group of synapses, which includes a first portion and a second portion. The control system selects one of a group of operating modes. The group of neurons processes information. The group of synapses provide connectivity to each of the group of neurons. During a first operating mode of the group of operating modes, the first portion of the group of synapses is enabled and the second portion of the group of synapses is enabled. During a second operating mode of the group of operating modes, the first portion of the group of synapses is enabled and the second portion of the group of synapses is disabled.

Abstract: An apparatus and method are described for a neuromorphic processor design in which neuron timing information is duplicated on a neuromorphic core.

Abstract: An apparatus for providing on-chip voltage-regulated power includes a switched capacitor voltage conversion circuit that receives an elevated power demand signal and operates at a base rate when the elevated power demand signal is not active and at an elevated rate when the elevated power demand signal is active. The switched capacitor voltage conversion circuit comprises an auxiliary set of transistors that are disabled, when the elevated power demand signal is not active and enabled, when the elevated power demand signal is active. The apparatus may also include a droop detection circuit that monitors a monitored power signal and activates the elevated power demand signal in response to the monitored power signal dropping below a selected voltage level. The monitored power signal may be a voltage input provided by an input power supply for the switched capacitor voltage conversion circuit. A corresponding method is also disclosed herein.

Abstract: Systems and methods for an interconnection scheme for reconfigurable neuromorphic hardware are disclosed. A neuromorphic processor may include a plurality of corelets, each corelet may include a plurality of synapse arrays and a neuron array. Each synapse array may include a plurality of synapses and a synapse array router coupled to synapse outputs in a synapse array. Each synapse may include a synapse input, synapse output; and a synapse memory. A neuron array may include a plurality of neurons, each neuron may include a neuron input and a neuron output. Each synapse array router may include a first logic to route one or more of the synapse outputs to one or more of the neuron inputs.

Abstract: An apparatus for providing on-chip voltage-regulated power includes a switched capacitor voltage conversion circuit that receives an elevated power demand signal and operates at a base rate when the elevated power demand signal is not active and at an elevated rate when the elevated power demand signal is active. The switched capacitor voltage conversion circuit comprises an auxiliary set of transistors that are disabled, when the elevated power demand signal is not active and enabled, when the elevated power demand signal is active. The apparatus may also include a droop detection circuit that monitors a monitored power signal and activates the elevated power demand signal in response to the monitored power signal dropping below a selected voltage level. The monitored power signal may be a voltage input provided by an input power supply for the switched capacitor voltage conversion circuit. A corresponding method is also disclosed herein.

Abstract: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array.

Abstract: Neuromorphic computational circuitry is disclosed that includes a cross point resistive network and line control circuitry. The cross point resistive network includes variable resistive units. One set of the variable resistive units is configured to generate a correction line current on a conductive line while other sets of the variable resistive units generate resultant line currents on other conductive lines. The line control circuitry is configured to receive the line currents from the conductive lines and generate digital vector values. Each of the digital vector values is provided in accordance with a difference between the current level of a corresponding resultant line current and a current level of the correction line current. In this manner, the digital vector values are corrected by the current level of the correction line current in order to reduce errors resulting from finite on to off conductance state ratios.

Abstract: This disclosure relates generally to resistive memory systems. The resistive memory systems may be utilized to implement neuro-inspired learning algorithms with full parallelism. In one embodiment, a resistive memory system includes a cross point resistive network and switchable paths. The cross point resistive network includes variable resistive elements and conductive lines. The conductive lines are coupled to the variable resistive elements such that the conductive lines and the variable resistive elements form the cross point resistive network. The switchable paths are connected to the conductive lines so that the switchable paths are operable to selectively interconnect groups of the conductive lines such that subsets of the variable resistive elements each provide a combined variable conductance. With multiple resistive elements in the subsets, process variations in the conductances of the resistive elements average out.

Abstract: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array.

Abstract: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array.

Abstract: Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a universal substrate of adaptation. One embodiment comprises a neurosynaptic device including a memory device that maintains neuron attributes for multiple neurons. The module further includes multiple bit maps that maintain incoming firing events for different periods of delay and a multi-way processor. The processor includes a memory array that maintains a plurality of synaptic weights. The processor integrates incoming firing events in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes and the synaptic weights maintained.

Abstract: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array.

Abstract: Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a universal substrate of adaptation. One embodiment comprises a neurosynaptic device including a memory device that maintains neuron attributes for multiple neurons. The module further includes multiple bit maps that maintain incoming firing events for different periods of delay and a multi-way processor. The processor includes a memory array that maintains a plurality of synaptic weights. The processor integrates incoming firing events in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes and the synaptic weights maintained.

Abstract: An apparatus for providing on-chip voltage-regulated power includes a switched capacitor voltage conversion circuit that receives an elevated power demand signal and operates at a base rate when the elevated power demand signal is not active and at an elevated rate when the elevated power demand signal is active. The switched capacitor voltage conversion circuit comprises an auxiliary set of transistors that are disabled, when the elevated power demand signal is not active and enabled, when the elevated power demand signal is active. The apparatus may also include a droop detection circuit that monitors a monitored power signal and activates the elevated power demand signal in response to the monitored power signal dropping below a selected voltage level. The monitored power signal may be a voltage input provided by an input power supply for the switched capacitor voltage conversion circuit. A corresponding method is also disclosed herein.

Abstract: Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a neural network. One embodiment comprises maintaining neuron attributes for multiple neurons and maintaining incoming firing events for different time steps. For each time step, incoming firing events for said time step are integrated in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes maintained. For each time step, the neuron attributes maintained are updated in parallel based on the integrated incoming firing events for said time step.

Abstract: This disclosure relates generally to resistive memory systems. The resistive memory systems may be utilized to implement neuro-inspired learning algorithms with full parallelism. In one embodiment, a resistive memory system includes a cross point resistive network and switchable paths. The cross point resistive network includes variable resistive elements and conductive lines. The conductive lines are coupled to the variable resistive elements such that the conductive lines and the variable resistive elements form the cross point resistive network. The switchable paths are connected to the conductive lines so that the switchable paths are operable to selectively interconnect groups of the conductive lines such that subsets of the variable resistive elements each provide a combined variable conductance. With multiple resistive elements in the subsets, process variations in the conductances of the resistive elements average out.

Abstract: Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a neural network. One embodiment comprises maintaining neuron attributes for multiple neurons and maintaining incoming firing events for different time steps. For each time step, incoming firing events for said time step are integrated in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes maintained. For each time step, the neuron attributes maintained are updated in parallel based on the integrated incoming firing events for said time step.

Abstract: A configurable-voltage converter circuit that may be CMOS and an integrated circuit chip including the converter circuit and method of operating the IC chip and circuit. A transistor totem, e.g., of 6 or more field effect transistors, PFETs and NFETs, connected (PNPNPN) between a first supply (Vin) line and a supply return line. A first switching capacitor is connected between first and second pairs of totem PN FETs pair of transistors. A second switching capacitor is connected between the second and a third pair of totem FETs. A configuration control selectively switches both third FETs off to float the connected end of the second capacitor, thereby switching voltage converter modes.

Abstract: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array.