Figure 3 From Decompiling X86 Deep Neural Network Executables

Decompiling X86 Deep Neural Network Executables R Reverseengineering We propose btd, a decompiler for dnn executables. given a (stripped) executable compiled from a dnn model, we propose a three step approach for full recovery of dnn op erators, network topology, dimensions, and parameters. We present btd (bin to dnn), a decompiler for deep neural network (dnn) executables. btd takes dnn executables and outputs full model specifications, including types of dnn operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models.

Pdf Decompiling X86 Deep Neural Network Executables This paper addresses the challenges of executing quantized deep learning models on diverse hardware platforms by proposing an augmented compiler approach that created a new dialect called quantized neural network (qnn) that extends the compiler's internal representation with a quantization context. Btd takes dnn executables (running on x86 cpus) compiled by dnn compilers (e.g., tvm, glow, and nnfusion) and outputs full model specifications, including types of dnn operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. We ran our evaluation experiments on a server equipped with intel xeon cpu e5 2683, 256gb ram, and an nvidia geforce rtx 2080 gpu. logging and filtering all traces for all dnn executables in the evaluation takes more than a week and consumes nearly 1tb disk storage. We present btd (bin to dnn), a decompiler for deep neural network (dnn) executables. btd takes dnn executables and outputs full model specifications, including types of dnn operators,.

Table 2 From Decompiling X86 Deep Neural Network Executables Semantic We ran our evaluation experiments on a server equipped with intel xeon cpu e5 2683, 256gb ram, and an nvidia geforce rtx 2080 gpu. logging and filtering all traces for all dnn executables in the evaluation takes more than a week and consumes nearly 1tb disk storage. We present btd (bin to dnn), a decompiler for deep neural network (dnn) executables. btd takes dnn executables and outputs full model specifications, including types of dnn operators,. We present btd (bin to dnn), a decompiler for deep neural network (dnn) executables. btd takes dnn executables and outputs full model specifications, including types of dnn operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. Openreview is a long term project to advance science through improved peer review with legal nonprofit status. we gratefully acknowledge the support of the openreview sponsors. © 2025 openreview. Btd is able to recover full model specification (including operators, topologies, dimensions, and parameters) from dnn executable. we train a lstm model to map assembly functions to dnn operators. Workflow btd consists of 3 steps: operator recovery, topology recovery, dimension & parameter recovery. • btd is able to recover full model specification (including operators, topologies, dimensions, and parameters) from dnn executable.

Figure 1 From Decompiling X86 Deep Neural Network Executables We present btd (bin to dnn), a decompiler for deep neural network (dnn) executables. btd takes dnn executables and outputs full model specifications, including types of dnn operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. Openreview is a long term project to advance science through improved peer review with legal nonprofit status. we gratefully acknowledge the support of the openreview sponsors. © 2025 openreview. Btd is able to recover full model specification (including operators, topologies, dimensions, and parameters) from dnn executable. we train a lstm model to map assembly functions to dnn operators. Workflow btd consists of 3 steps: operator recovery, topology recovery, dimension & parameter recovery. • btd is able to recover full model specification (including operators, topologies, dimensions, and parameters) from dnn executable.

Figure 1 From Decompiling X86 Deep Neural Network Executables Btd is able to recover full model specification (including operators, topologies, dimensions, and parameters) from dnn executable. we train a lstm model to map assembly functions to dnn operators. Workflow btd consists of 3 steps: operator recovery, topology recovery, dimension & parameter recovery. • btd is able to recover full model specification (including operators, topologies, dimensions, and parameters) from dnn executable.