An apparatus includes a memory array. The array in turn includes a plurality of word lines, a plurality of bit line pairs intersecting the plurality of word lines at a plurality of cell locations, and a plurality of memory cells, coupled to the plurality of word lines and the plurality of bit line pairs, and located at the plurality of cell locations. A plurality of word line drivers are coupled to the plurality of word lines, a dynamic voltage boost is coupled to the memory array, and a controller is coupled to the plurality of word line drivers and the dynamic voltage boost. The controller is configured to cause the dynamic voltage boost to boost the cells during a multiply accumulate operation.

An apparatus includes a memory array. The array in turn includes a plurality of word lines, a plurality of bit line pairs intersecting the plurality of word lines at a plurality of cell locations, and a plurality of memory cells, coupled to the plurality of word lines and the plurality of bit line pairs, and located at the plurality of cell locations. A plurality of word line drivers are coupled to the plurality of word lines, a dynamic voltage boost is coupled to the memory array, and a controller is coupled to the plurality of word line drivers and the dynamic voltage boost. The controller is configured to cause the dynamic voltage boost to boost the cells during a multiply accumulate operation.

Systems, apparatuses, and methods related to a neuron built with posits are described. An example system may include a memory device and the memory device may include a plurality of memory cells. The plurality of memory cells can store data including a bit string in an analog format. A neuromorphic operation can be performed on the data in the analog format. The example system may include an analog to digital converter coupled to the memory device. The analog to digital converter may convert the bit string in the analog format stored in at least one of the plurality of memory cells to a format that supports arithmetic operations to a particular level of precision.

Systems, apparatuses, and methods related to a neuron built with posits are described. An example system may include a memory device and the memory device may include a plurality of memory cells. The plurality of memory cells can store data including a bit string in an analog format. A neuromorphic operation can be performed on the data in the analog format. The example system may include an analog to digital converter coupled to the memory device. The analog to digital converter may convert the bit string in the analog format stored in at least one of the plurality of memory cells to a format that supports arithmetic operations to a particular level of precision.

Methods and apparatus for filtering a signal using a current-mode filter circuit implementing source degeneration. An example filter circuit generally includes an input node; an output node; a power supply node; a first transistor comprising a drain coupled to the input node; a second transistor comprising a drain coupled to the output node and comprising a gate coupled to a gate of the first transistor; a capacitive element coupled between the drain of the first transistor and the power supply node; a first resistive element coupled between the drain and the gate of the first transistor; a first source degeneration element coupled between a source of the first transistor and the power supply node; and a second source degeneration element coupled between a source of the second transistor and the power supply node.

Methods and apparatus for filtering a signal using a current-mode filter circuit implementing source degeneration. An example filter circuit generally includes an input node; an output node; a power supply node; a first transistor comprising a drain coupled to the input node; a second transistor comprising a drain coupled to the output node and comprising a gate coupled to a gate of the first transistor; a capacitive element coupled between the drain of the first transistor and the power supply node; a first resistive element coupled between the drain and the gate of the first transistor; a first source degeneration element coupled between a source of the first transistor and the power supply node; and a second source degeneration element coupled between a source of the second transistor and the power supply node.

The techniques and circuits, described herein, include solutions for error compensation in source measurement units (SMUs). An example SMU is capable of both sourcing current to a device under test (DUT) and measuring current through the DUT. An SMU may include a sensing resistor coupled in series with the DUT. A voltage across the sensing resistor may be measured by a current sensing amplifier in order to determine the output current through the DUT. In practice, the resistance of the sensing resistor may vary depending on manufacturing tolerances, etc. A gain of the current sensing amplifier may be calibrated in order to compensate for sensing resistor variance, which increases the accuracy with which current to the DUT can be sourced and measured.

An example apparatus includes: resistor ladder circuitry including a plurality of intermediate voltage nodes; a first plurality of switches having inputs coupled to a plurality of intermediate voltage nodes and having outputs; first level decoder circuitry configured to: receive a set of input bits; and open or close ones of the first plurality of switches based on a first subset of the input bits; a second plurality of switches having inputs coupled to the outputs of the first plurality of switches and having outputs coupled to a common node; and second level decoder circuitry configured to: receive the set of input bits; and open or close ones of the second plurality of switches based on a second subset of the input bits, the first and the second subsets sharing one of the input bits, wherein the output voltage is to be coupled to the common node.

A neurostimulator incorporating a novel chip design that uses the principle of redundant signal crossfiring to overcome electronic component mismatch error in general and transistor mismatch error in particular, to yield superior quality neurostimulation signal generation, useful in enhancing the bidirectional human-machine interface in prosthesis operation for the restoration of somatosensation for an amputee.

A neurostimulator incorporating a novel chip design that uses the principle of redundant signal crossfiring to overcome electronic component mismatch error in general and transistor mismatch error in particular, to yield superior quality neurostimulation signal generation, useful in enhancing the bidirectional human-machine interface in prosthesis operation for the restoration of somatosensation for an amputee.