wmkenyon/BinaryTranslation-

AI Summary: The task involves optimizing a binary translation project from x86 to RISC-V using neural networks (NN) or transformers. The current issue is resolving a GPU utilization conflict in PyTorch, preventing efficient NN training. The solution requires adapting the code to utilize the GPU correctly using the .cuda() function, enabling faster model training and validation.

AI Summary: Implement backpropagation for an LSTM model within a PyTorch framework to enable training of the model for x86 to RISC-V binary translation. This is a step in a larger project aiming to create an x86-compliant RISC-V processor without a cross-compiler, using either a large neural network or a transformer-based approach.

AI Summary: Implement backpropagation for two neural network architectures (a large neural network and a transformer-based model) to enable training for x86 to RISC-V binary translation. This is a crucial step in building a system that allows a RISC-V processor to execute x86 binaries without a cross-compiler.

AI Summary: Implement backpropagation for a simple neural network using PyTorch. This is a foundational step in a larger project aiming to translate x86 binary code to RISC-V using neural networks, ultimately enabling x86 compatibility on a RISC-V processor without a cross-compiler.