US 12,762,412 B2 · Adaptive quantization method for analog in-memory computing systems

Abstract

One or more systems, methods and/or machine-readable mediums are described herein for adaptively quantizing the parameters that are aimed to be deployed on in-memory computing systems based on magnetic memory devices. The method includes quantizing parameters based on a conductance shift sensing process, a sensed conductance shift value, a conductance shift lookup table, and a parameter to be quantized. The parameter can be quantized by using a value recorded in the lookup table, such as rounding to the nearest value. The lookup table can be generated by the conductance shift sensing process which can enabling recording of a sensed conductance of a magnetic memory device and an associated device state. The conductance shift sensing process can set the magnetic memory device (MMD) to different states and can measure the conductance shift of the MMD, caused by the state setting, using suitable equipment.

Claims20

Dependent claims

A system, comprising:

at least one memory that stores computer executable components;
and
at least one processor that executes the computer executable components stored in the at least one memory to perform operations comprising:
determining conductance errors of memory cells of a magnetic memory device (MMD) based on a specified state of the MMD and based on sensed conductances of the memory cells at the specified state, resulting in a plurality of known conductance shifts of the MMD;
and
tuning a plurality of initially calibrated parameters of a neural network, the tuning comprising mapping the plurality of initially calibrated parameters to the memory cells using the plurality of known conductance shifts of the MMD, resulting in a set of primarily trained parameters.

3 more elements

A method, comprising:

quantizing neural network parameters at a magnetic memory device (MMD);
executing first calibrations of the parameters, wherein the first calibrations comprise determining a first error resulting from a first forward calculation pass of first data using the parameters and backpropagating the first error to update the parameters, wherein the first calibrations are executed using selectively increasing levels of noise added to the parameters prior to the first forward calculation pass, and wherein the executing the first calibrations results in initially calibrated parameters;
determining a conductance error of the MMD, based on a specified state of the MMD and based on a sensed conductance of the MMD, resulting in a determined conductance shift of the MMD;
and
tuning the initially calibrated parameters using the determined conductance shifts of the MMD, resulting in primarily trained parameters.

2 more elements

Dependencies are inferred from the claim text.

Adaptive quantization method for analog in-memory computing systems

Life of this patent

Adaptive quantization method for analog in-memory computing systems

Life of this patent

Abstract

Claims20