Systems, computer program products, and methods are described herein for software compiler integrity verification. The present invention is configured to retrieve, from a source code repository, a source code; process, using a first build machine, the source code into a first object code; process, using a second build machine, the source code into a second object code; initiate an integrity verification engine on the first object code and the second object code; decompile, using the integrity verification engine, the first object code to create a first decompiled object code and the second object code to create a second decompiled object code; compare the first decompiled object code with the second decompiled object code; determine a match between the first decompiled object code and the second decompiled object code; and transmit an approval notification.

Systems and methods for generating SLO specifications using annotations are generally described. In various examples, first source code associated with a first computer-implemented service is received. In various cases, a first annotation in the first source code may be received. The first annotation may include first metadata defining a name of an SLO specification. A second annotation in the first source code may be received. The second annotation may include second metadata defining a service-level objective (SLO) of a first aspect of the first computer-implemented service. In some cases, the first computer-implemented service may be executed using the first source code. In various examples, the SLO specification may be generated based on the first annotation and the second annotation.

Vehicle developer devices, systems and methods are disclosed. In one embodiment, a vehicle developer device includes a plurality of electronic control units, a plurality peripheral devices communicatively coupled to the plurality of electronic control units, wherein one or more individual peripheral devices of the plurality of peripheral devices is a physical representation of an actual vehicle peripheral device, and a management computing device including one or more processors and a memory device storing computer-readable instructions. The vehicle developer device receives one or more sets of software instructions, compiles the one or more sets of software instructions for execution by at least one electronic control unit of the plurality of electronic control units, and receives output from one or more of: 1) at least one electronic control unit of the plurality of electronic control units and 2) at least one peripheral device of the plurality of peripheral devices.

A processing system includes a hardware translation lookaside buffer (TLB) retry loop that retries virtual memory address to physical memory address translation requests from a software client independent of a command from the software client. In response to a retry response notification at the TLB, a controller of the TLB waits for a programmable delay period and then retries the request without involvement from the software client. After a retry results in a hit at the TLB, the controller notifies the software client of the hit. Alternatively, if a retry results in an error at the TLB, the controller notifies the software client of the error and the software client initiates error handling.

According to some illustrative embodiments of the invention, a method is performed that includes using a representation of a computer software program, using identified addresses which correspond to a part of the representation, and converting the representation into a created binary program, which includes reserving spaces at the identified addresses in the created binary program's address space at the same addresses as the identified addresses in the representation.

Generally described, one or more aspects of the present application relate to improving the process of generating and deploying software applications in a network environment, particularly software applications that incorporate or rely upon machine learning models. More specifically, the present disclosure provides specific user interface features and associated computer-implemented features that may effectively, from a user's perspective, remove most of the complexities associated with writing and deploying code and developing and improving machine learning models. For example, the present disclosure may provide user-friendly visual building blocks that allow users to build and customize machine learning workflows that can then be turned into a full software application and optimized and deployed at target destinations of the users' choice.

Processor hardware detects when memory aliasing occurs, and assures proper operation of the code even in the presence of memory aliasing. The processor defines a special store instruction that is different from a regular store instruction. The special store instruction is used in regions of the computer program where memory aliasing may occur. Because the hardware can detect and correct for memory aliasing, this allows a compiler to make optimizations such as register promotion even in regions of the code where memory aliasing may occur.

A neural transformer model with attention is trained to predict candidates to complete a line of source code with a zero-inference capability. The model is trained on an unsupervised training dataset that includes features from source code written in multiple programming languages. The features include a file-level context and a local context, where the file-level context includes a global context, a class context, a function context, and/or a method context for each class, function and/or method of the source code programs used in the training dataset. The local context includes method bodies, function bodies, and/or stand-alone code of main method routines. From these features, the model is able to learn to predict an ordered sequence of code elements that complete a line of source code in a programming language seen and not seen during training.

Technologies for performing flexible code acceleration on a computing device includes initializing an accelerator virtual device on the computing device. The computing device allocates memory-mapped input and output (I/O) for the accelerator virtual device and also allocates an accelerator virtual device context for a code to be accelerated. The computing device accesses a bytecode of the code to be accelerated and determines whether the bytecode is an operating system-dependent bytecode. If not, the computing device performs hardware acceleration of the bytecode via the memory-mapped I/O using an internal binary translation module. However, if the bytecode is operating system-dependent, the computing device performs software acceleration of the bytecode.

In one embodiment, an apparatus comprises a fabric controller of a first computing node. The fabric controller is to receive, from a second computing node via a network fabric that couples the first computing node to the second computing node, a request to execute a kernel on a field-programmable gate array (FPGA) of the first computing node; instruct the FPGA to execute the kernel; and send a result of the execution of the kernel to the second computing node via the network fabric.