This research paper extends our previously introduced concepts of a multi-layer perceptron (MLP)-based CPU. We present exhaustive details on every facet of the design, from historical motivations and theoretical underpinnings to transistor-level implementations, advanced pipeline structures, memory hierarchies, and future-looking innovations such as approximate perceptron logic or on-chip training. While historically, threshold logic was overshadowed by the dominance of CMOS gate-level designs; this paper demonstrates that a fully perceptron-based CPU—dubbed IC 616 Ultra-MLP—can theoretically implement all standard computing tasks by assigning appropriate weights and biases to arrays of threshold units. We thoroughly analyze potential advantages, substantial challenges, and the interplay between neural and digital paradigms. This paper aims to be an exhaustive reference for researchers, students, and architects intrigued by bridging neural networks and CPU design in the most literal sense.