The determination of upper bounds on execution times, commonly called worst-case execution times (WCETs), is a necessary step in the development and validation process for hard real-time systems. This problem is hard if the underlying processor architecture has components, such as caches, pipelines, branch prediction, and other speculative components. This article describes different approaches to this problem and surveys several commercially available tools ¹ and research prototypes.

CGRAs are promising as accelerators due to their improved energy-efficiency compared to FPGAs. Existing CGRAs support reconfigurability for operations, but not communications because of the static neighbor-to-neighbor interconnect, leading to both performance loss and increased complexity of the compiler. In this paper, we introduce HyCUBE, a novel CGRA architecture with a reconfigurable interconnect providing single-cycle communications between distant FUs, resulting in a new formulation of the application mapping problem that leads to the design of an efficient compiler. HyCUBE achieves 1.5X and 3X better performance-per-watt compared to a CGRA with standard NoC and a CGRA with neighbor-to-neighbor connectivity, respectively.

IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself. ARM big.LITTLE architecture is at the heart of prevalent commercial edge devices. It comprises of single-ISA heterogeneous cores grouped into multiple homogeneous clusters that enable power and performance trade-offs. All cores are expected to be simultaneously employed in inference to attain maximal throughput. However, high communication overhead involved in parallelization of computations from convolution kernels across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs pipelined design to split convolutional layers across clusters while limiting parallelization of their respective kernels to the assigned cluster. We develop a performance-prediction model that utilizes only the convolutional layer descriptors to predict the execution time of each layer individually on all permitted core configurations (type and count). Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in a 39% higher throughput than the highest antecedent throughput.