In the hardware design space exploration process, it is critical to optimize both hardware
parameters and algorithm-to-hardware mappings. Previous work has largely approached …

The hardware design space is high-dimensional and discrete. Systematic and efficient
exploration of this space has been a significant challenge. Central to this problem is the …

The utilization of Bayesian methods has been widely acknowledged as a viable solution for
tackling various challenges in electronic integrated circuit (IC) design under stochastic …

Achieving high performance for machine learning domain-specific accelerators requires the
careful choice of a mapping from an algorithm to an accelerator. Most algorithms for finding …

Tensor operations play a central role in many applications, such as machine learning, signal
processing, and dense linear algebra. These tensor operations are increasing significantly …

The architectural design-space exploration (or DSE) process—whether manual or
automated—benefits greatly from knowing the limits of the metrics of interest in advance …

The process of hardware design space exploration requires both hardware parameters and
mappings from the algorithm onto the target hardware to be discovered and optimized …