This project takes a learning-based approach to the acceleration of approximate programs. We introduce a program transformation, called the “Parrot transformation,” that selects and trains a neural network to mimic a region of imperative code. After the learning transformation phase, the compiler replaces the original code with an invocation of a low-power accelerator called a “neural processing unit” (NPU). The NPU is tightly coupled to the processor’s speculative pipeline, since many of the accelerated code regions are small. Since neural networks produce inherently approximate results, we define a programming model that allows programmers to identify approximable code regions-code that can produce imprecise but acceptable results. Mimicking approximable code regions with an NPU is both faster and more energy efficient than executing the original code. For a set of diverse applications, NPU acceleration provides an average whole-application speedup of 2.3x and energy savings of 3.0x with quality loss at most 9.6%.
This is a joint project at University of Washington, Microsoft Research, and The University of Texas at Austin.
Neural Acceleration for General-Purpose Approximate Programs. Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. MICRO 2012.
Towards Neural Acceleration for General-Purpose Approximate Computing. Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. WEED 2012.
Addressing Dark Silicon Challenges with Disciplined Approximate Computing. Hadi Esmaeilzadeh, Adrian Sampson, Michael Ringenburg, Luis Ceze, Dan Grossman, and Doug Burger. DaSi 2012. Slides.