Explore a groundbreaking processor microarchitecture called Laplace, designed for tracking uncertainty in computing systems. Delve into the innovative methods for in-processor distribution representations that approximate probability distributions, similar to how floating-point numbers approximate real-valued numbers. Learn how Laplace executes unmodified RISC-V binaries while tracking uncertainty, and examine its performance across 21 benchmarks from various domains including variational quantum algorithms, sensor data processing, and materials properties modeling. Discover how Laplace achieves comparable accuracy to Monte Carlo methods with significantly fewer instructions, and outperforms state-of-the-art alternatives like PaCAL and the NIST Uncertainty Machine. Understand the benefits of Laplace's approach, which requires minimal changes to existing software while providing powerful uncertainty tracking capabilities. Gain insights into this cutting-edge technology that has already been incorporated into a commercial product, potentially revolutionizing how we handle uncertainty in computational systems processing data from the physical world. Read more