Chisel: Reliability- and Accuracy-Aware Optimization of Approximate Computational Kernels

The accuracy of an approximate computation is the distance between the result that the computation produces and the corresponding fully accurate result. The reliability of the computation is the probability that it will produce an acceptably accurate result. Emerging approximate hardware platforms provide approximate operations that, in return for reduced energy consumption and/or increased performance, exhibit reduced reliability and/or accuracy. We present Chisel, a system for reliability- and accuracy-aware optimization of approximate computational kernels that run on approximate hardware platforms. Given a combined reliability and/or accuracy specification, Chisel automatically selects approximate kernel operations to synthesize an approximate computation that minimizes energy consumption while satisfying its reliability and accuracy specification. We evaluate Chisel on five applications from the image processing, scientific computing, and financial analysis domains. The experimental results show that our implemented optimization algorithm enables Chisel to optimize our set of benchmark kernels to obtain energy savings from 8.7% to 19.8% compared to the fully reliable kernel implementations while preserving important reliability guarantees.National Science Foundation (U.S.) (Grant CCF-1036241)National Science Foundation (U.S.) (Grant CCF-1138967)National Science Foundation (U.S.) (Grant IIS-0835652)United States. Dept. of Energy (Grant DE-SC0008923)United States. Defense Advanced Research Projects Agency (Grant FA8650-11-C-7192)United States. Defense Advanced Research Projects Agency (Grant FA8750-12-2-0110)United States. Defense Advanced Research Projects Agency (Grant FA-8750-14-2-0004

Verified integrity properties for safe approximate program transformations

Approximate computations (for example, video, audio, and image processing, machine learning, and many scientific computations) have the freedom to generate a range of acceptable results. Approximate program transformations (for example, task skipping and loop perforation) exploit this freedom to produce computations that can execute at a variety of points in an underlying accuracy versus performance trade-off space. One potential concern is that these transformations may change the semantics of the program and therefore cause the program to crash, perform an illegal operation, or otherwise violate its integrity. We investigate how verifying integrity properties -- key correctness properties that the transformed computation must respect -- can enable the safe application of approximate program transformations. We present experimental results from a compiler that verifies integrity properties of perforated loops to enable the safe application of loop perforation.National Science Foundation (U.S.) (Grant CCF-0811397)National Science Foundation (U.S.) (Grant CCF-0905244)National Science Foundation (U.S.) (Grant CCF-1036241)National Science Foundation (U.S.) (Grant IIS-0835652)United States. Defense Advanced Research Projects Agency (Grant FA8650-11-C-7192)United States. Defense Advanced Research Projects Agency (Grant FA8750-12-2-0110)United States. Dept. of Energy (Grant DE-SC0005288

Neural network computing using on-chip accelerators

The use of neural networks, machine learning, or artificial intelligence, in its broadest and most controversial sense, has been a tumultuous journey involving three distinct hype cycles and a history dating back to the 1960s. Resurgent, enthusiastic interest in machine learning and its applications bolsters the case for machine learning as a fundamental computational kernel. Furthermore, researchers have demonstrated that machine learning can be utilized as an auxiliary component of applications to enhance or enable new types of computation such as approximate computing or automatic parallelization. In our view, machine learning becomes not the underlying application, but a ubiquitous component of applications. This view necessitates a different approach towards the deployment of machine learning computation that spans not only hardware design of accelerator architectures, but also user and supervisor software to enable the safe, simultaneous use of machine learning accelerator resources. In this dissertation, we propose a multi-transaction model of neural network computation to meet the needs of future machine learning applications. We demonstrate that this model, encompassing a decoupled backend accelerator for inference and learning from hardware and software for managing neural network transactions can be achieved with low overhead and integrated with a modern RISC-V microprocessor. Our extensions span user and supervisor software and data structures and, coupled with our hardware, enable multiple transactions from different address spaces to execute simultaneously, yet safely. Together, our system demonstrates the utility of a multi-transaction model to increase energy efficiency improvements and improve overall accelerator throughput for machine learning applications