Abstract: Described is a neuromorphic system implemented in hardware that implements neuron membrane potential update based on the leaky integrate and fire (LIF) model. The system further models synapse weights update based on the spike time-dependent plasticity (STDP) model. The system includes an artificial neural network in which the update scheme of neuron membrane potential and synapse weight are effectively defined and implemented.

Abstract: A method and system are provided for updating a neuron membrane potential in a spike time dependent plasticity model in a Neuromorphic system. The method includes approximating a shape of an analog spike signal from an axon input using a hardware-based digital axon timer. The method further includes generating a first intermediately updated neuron membrane potential value from a current axon timer value, a current synapse weight value and a current neuron membrane potential value using a first look-up table and an accumulator. The method also includes generating a second intermediately updated neuron membrane potential value with a leak decay effect using a second look-up table and the first intermediately updated neuron membrane potential value. The method additionally includes generating a final updated neuron membrane potential value based on a comparison of the second intermediately updated neuron membrane potential value with a neuron fire threshold level using a comparator.

Abstract: A neuromorphic synapse array is provided which ensures that a neuron model as such McCulloch-Pitts is dependent on nonlinearity with a single polarity weight cell. The neuromorphic synapse array includes a plurality of synaptic array cells, a plurality of operation column arrays, and a reference column array. The synaptic array cells respectively have a single polarity synapse weight and are classified into operation synapse cells and reference synapse cells for shifting a product-sum of the operation synapse cells. The operation column arrays are defined by the operation synapse cells aligned in column of the array. The reference column array is defined by the reference synapse cells aligned in column of the array.

Abstract: Embodiments are directed to a driver circuit including a first amplifier having a voltage follower configured to control a first node to maintain a voltage of the first node at a constant value. By maintaining the first node voltage, the first amplifier having the voltage follower is further configured to have a first amplifier output current into the first node at a value without the effect of the voltage fluctuation. The driver circuit further includes a second amplifier configured to control a second node, wherein the second amplifier is in a current mirror configuration with respect to the first amplifier such that a second amplifier current output is a highly precise mirror of the first amplifier current output.

Abstract: Embodiments include methods and systems of neuron leaky integrate and fire circuit (NLIFC). Aspects include: receiving an input current having both AC component and DC component at an input terminal of the NLIFC, extracting AC component of input current, generating a number of swing voltages at a swing node using extracted AC component of the input current, transferring charge from a pull-up node to a neuron membrane potential (NP) node through an integration diode and a pull-up diode to raise a voltage at NP node over an integration capacitor gradually and the voltage at NP node shows integration value of AC component of input current, implementing leaky decay function of the neuron leaky integrate and fire circuit, detecting a timing of neuron fire using an analog comparator, resetting a neuron membrane potential level for a refractory period after neuron fire, and generating fire output signal of the NLIFC.

Abstract: Described is a neuromorphic system implemented in hardware that implements neuron membrane potential update based on the leaky integrate and fire (LIF) model. The system further models synapse weights update based on the spike time-dependent plasticity (STDP) model. The system includes an artificial neural network in which the update scheme of neuron membrane potential and synapse weight are effectively defined and implemented.

Abstract: Embodiments include methods and systems of neuron leaky integrate and fire circuit (NLIFC). Aspects include: receiving an input current having both AC component and DC component at an input terminal of the NLIFC, extracting AC component of input current, generating a number of swing voltages at a swing node using extracted AC component of the input current, transferring charge from a pull-up node to a neuron membrane potential (NP) node through an integration diode and a pull-up diode to raise a voltage at NP node over an integration capacitor gradually and the voltage at NP node shows integration value of AC component of input current, implementing leaky decay function of the neuron leaky integrate and fire circuit, detecting a timing of neuron fire using an analog comparator, resetting a neuron membrane potential level for a refractory period after neuron fire, and generating fire output signal of the NLIFC.

Abstract: Described is a neuromorphic system implemented in hardware that implements neuron membrane potential update based on the leaky integrate and fire (LIF) model. The system further models synapse weights update based on the spike time-dependent plasticity (STDP) model. The system includes an artificial neural network in which the update scheme of neuron membrane potential and synapse weight are effectively defined and implemented.

Abstract: A memory cell structure includes a plurality of write lines arranged for writing a synapse state to a synapse memory cell including a plurality of cell components each including at least one unit cell, each of the plurality of write lines being used for writing the synapse state by writing a first set of states to a corresponding cell component of the plurality of cell components by writing one of a second set of states to each unit cell included in the corresponding cell component, and the first set depending on the second set.

Abstract: A synapse memory and a method for reading a weight value stored in a synapse memory are provided. The synapse memory includes a memory device configured to store a weight value. The memory device includes a read terminal, a write terminal, and a common terminal, the read terminal being configured to receive a read signal, the write terminal being configured to receive a write signal, and the common terminal being configured to output an output signal from the memory device. The synapse memory also includes a write transistor provided between the write terminal of the memory device and a write signal line configured to send the write signal. The synapse memory further includes a common transistor provided between the common terminal of the memory device and one of the dendrite lines.

Abstract: A synapse memory system includes: synapse memory cells provided at cross points of axon lines and dendrite lines, each synapse memory cell being associated with nonvolatile random-access memory (NVRAM), each synapse memory cell being configured to store a weight value according to an output level of a write signal; a write portion configured to write the weight value to each synapse memory cell, the write portion including a write driver and an output controller, the write driver being a digital driver configured to output the write signal to a subject synapse memory cell, the output controller being configured to control the output level of the write signal of the write driver; and read drivers configured to read the weight value stored in the synapse memory cells.

Abstract: A memory cell structure includes a plurality of write lines arranged for writing a synapse state to a synapse memory cell including a plurality of cell components each including at least one unit cell, each of the plurality of write lines being used for writing the synapse state by writing a first set of states to a corresponding cell component of the plurality of cell components by writing one of a second set of states to each unit cell included in the corresponding cell component, and the first set depending on the second set.

Abstract: A memory cell structure includes a plurality of write lines arranged for writing a synapse state to a synapse memory cell including a plurality of cell components each including at least one unit cell, each of the plurality of write lines being used for writing the synapse state by writing a first set of states to a corresponding cell component of the plurality of cell components by writing one of a second set of states to each unit cell included in the corresponding cell component, the first and second sets each having a predetermined number of states, and the first set depending on the second set, and a read line arranged for reading the synapse state from the synapse memory cell.

Abstract: A memory cell structure includes a plurality of write lines arranged for writing a synapse state to a synapse memory cell including a plurality of cell components each including at least one unit cell, each of the plurality of write lines being used for writing the synapse state by writing a first set of states to a corresponding cell component of the plurality of cell components by writing one of a second set of states to each unit cell included in the corresponding cell component, the first and second sets each having a predetermined number of states, and the first set depending on the second set, and a read line arranged for reading the synapse state from the synapse memory cell.

Abstract: A memory cell structure includes a synapse memory cell including plural cell components, each of the plural cell components including a unit cell, plural write lines arranged for writing a synapse state to the synapse memory cell, each of the plural write lines being used for writing one of a first set of a predetermined number of states to a corresponding cell component by writing one of a second set of the predetermined number of states to the unit cell included in the corresponding cell component, the first set depending on the second set and a number of the unit cell included in the corresponding cell component, and a read line arranged for reading the synapse state from the synapse memory cell, the read line being used for reading one of the first set of the predetermined number of states from all of the plural cell components simultaneously.

Abstract: A barrel shifter uses a sign magnitude to 2's complement converter to generate decoder signals for its cascaded multiplexer selectors. The sign input receives the shift direction and the magnitude input receives the shift amount. The sign magnitude to 2's complement converter computes an output result as a 2's complement of the shift amount using the shift direction as a sign input, assigns a first portion (most significant bit half) of the output result to a first decoder signal, and assigns a second portion (least significant bit half) of the output result to a second decoder signal. This encoding scheme allows the decoder circuits to be relatively simple, for example, 3-to-8 decoders for an implementation adapted to shift a 64-bit operand value rather than the 4-to-9 decoder required in a conventional barrel shifter, leading to faster operation, less area, and reduced power consumption.

Abstract: A memory cell structure includes a synapse memory cell including plural cell components, each of the plural cell components including a unit cell, plural write lines arranged for writing a synapse state to the synapse memory cell, each of the plural write lines being used for writing one of a first set of a predetermined number of states to a corresponding cell component by writing one of a second set of the predetermined number of states to the unit cell included in the corresponding cell component, the first set depending on the second set and a number of the unit cell included in the corresponding cell component, and a read line arranged for reading the synapse state from the synapse memory cell, the read line being used for reading one of the first set of the predetermined number of states from all of the plural cell components simultaneously.

Abstract: Methods and systems are provided for operating a neuromorphic system for generating neuron and synapse activities. The method includes: preparing at least one digital timer in the neuromorphic system, each of the at least one digital timers including multi-bit digital values; generating time signals using the at least one digital timer; emulating an analog waveform of a neuron spike; updating parameters of the neuromorphic system using the time signals and the current values of the parameters; presetting, using a processor, the digital values of the at least one digital timer to initial values when the spike input is provided to the node; and updating, using the processor, the digital values of the at least one digital timer with a specified amount when there is an absence of a spike input to the node.

Abstract: Embodiments include methods and systems of neuron leaky integrate and fire circuit (NLIFC). Aspects include: receiving an input current having both AC component and DC component at an input terminal of the NLIFC, extracting AC component of input current, generating a number of swing voltages at a swing node using extracted AC component of the input current, transferring charge from a pull-up node to a neuron membrane potential (NP) node through an integration diode and a pull-up diode to raise a voltage at NP node over an integration capacitor gradually and the voltage at NP node shows integration value of AC component of input current, implementing leaky decay function of the neuron leaky integrate and fire circuit, detecting a timing of neuron fire using an analog comparator, resetting a neuron membrane potential level for a refractory period after neuron fire, and generating fire output signal of the NLIFC.

Abstract: Embodiments are directed to a driver circuit including a first amplifier having a voltage follower configured to control a first node to maintain a voltage of the first node at a constant value. By maintaining the first node voltage, the first amplifier having the voltage follower is further configured to have a first amplifier output current into the first node at a value without the effect of the voltage fluctuation. The driver circuit further includes a second amplifier configured to control a second node, wherein the second amplifier is in a current mirror configuration with respect to the first amplifier such that a second amplifier current output is a highly precise mirror of the first amplifier current output.