The Mathworks, Inc.

Lightmatter, Inc.

Ayon Basumallik

Nikolay Mateev

Nicholas Christopher Harris

Darius Bunandar

Brett W Baker

Tomo Lazovich

Bradley D Dobbie

Tyler J Kenney

Martin B Z Forsythe

Aaditya Kalsi

Meng-Ju Wu

Cansu Demirkiran

Eric Hein

Ludmila Levkova

Rajeshwar Vanka

Lakshmi Nair

David Widemann

Nicholas Moore

Gongyu Wang

Prabhakar Kumar

Nicholas Dronen

David Walter

Hongjun Zheng

Described herein are techniques of training a machine learning model and performing inference using an analog processor. Some embodiments mitigate the loss in performance of a machine learning model resulting from a lower precision of an analog processor by using an adaptive block floating-point representation of numbers for the analog processor. Some embodiments mitigate the loss in performance of a machine learning model due to noise that is present when using an analog processor. The techniques involve training the machine learning model such that it is robust to noise.

Machine learning model training using an analog processor

Aspects of the present disclosure provide an aligned storage strategy for stripes within a long vector for a vector processor, such that the extra computation needed to track strides between input stripes and output stripes may be eliminated. As a result, the stripe locations are located in a more predictable memory access pattern such that memory access bandwidth may be improved and the tendency for memory error may be reduced.

Vector processor data storage

A photonic processor uses light signals and a residue number system (RNS) to perform calculations. The processor sums two or more values by shifting the phase of a light signal with phase shifters and reading out the summed phase with a coherent detector. Because phase winds back every 2&#x3c0; radians, the photonic processor performs addition modulo 2&#x3c0;. A photonic processor may use the summation of phases to perform dot products and correct erroneous residues. A photonic processor may use the RNS in combination with a positional number system (PNS) to extend the numerical range of the photonic processor, which may be used to accelerate homomorphic encryption (HE)-based deep learning.

Residue number system in a photonic matrix accelerator

The exemplary embodiments may provide an approach to finding and identifying the correlation between the invoking code and the invoked code by correlating the timestamps of contextual information of code in the invoking code and invoked code. As a result, developers have information during investigating the programs and can use the information to identify a region of interest to narrow down a performance problem in the invoking code efficiently. As a result, development productivity can be improved.

Correlating contextual information of invoking code in a programming language with contextual information of at least one invoked code in a different programming language

Extended types are defined for functions that are called by function handles in a programming environment. The extended types can be accessed and used by a computing system to improve compile-time and run-time performance of the computing system.

Computing performance for execution of functions called by function handles

Systems and methods for just-in-time compilation are disclosed. The systems and methods can be used to generate composite blocks, reducing program execution time. The systems and methods can include generating single-trace blocks during program execution. Upon satisfaction of a trigger criterion, single-trace blocks can be selected for compilation into a composite block. The trigger criterion can be a number of executions of a trigger block. Selecting the single-trace blocks can include identifying blocks reachable from the trigger block, selecting a subset of the reachable blocks, and selecting an entry point for the composite block. The composite block can be generated from the single-trace blocks and incorporated into the program control flow, such that the composite block is executed in place of the selected single-trace blocks.

Composite-trace just-in-time compilation

A device may identify a first compiled block with an original constraint and an additional constraint. The first compiled block may be identified based on a program counter value and may include compiled information relating to a first segment of program code, linking information associated with a second compiled block, and information distinguishing the original constraint from the additional constraint. The original constraint may relate to a type of variable used in the first segment of programming code. The additional constraint may relate to a variable used in a second segment of programming code associated with the second compiled block. The device may copy information of the first compiled block to generate a third compiled block that lacks the additional constraint. The device may execute the third compiled block to execute a program associated with the programming code.

Code generation and execution for dynamic programming languages

A device receives programming code, corresponding to a dynamic programming language, that is to be executed by a computing environment, and executes the programming code. When executing the programming code, the device maintains a program counter that identifies an execution location within the programming code, and select blocks of the programming code based on the program counter. The blocks correspond to segments of the programming code, and are associated with type-based constraints that relate to types of variables that are used by the block. When executing the programming code, the device also compiles the selected blocks, caches the compiled blocks along with the type-based constraints, generates linking information between certain ones of the compiled blocks based on the type-based constraints, and executes the compiled blocks in an order based on the program counter, the type-based constraints, and the linking information.

Growing community of inventors

Framingham, MA, United States of America

Ayon Basumallik