Silicon Dendrites: A Neuromorphic Hardware Architecture for Energy-Efficient Non-Linear Temporal Integration

Current Artificial Intelligence (AI) accelerators are limited by the Von Neumann bottleneck and the "Point-Neuron" simplification, where neurons are modeled as zero-dimensional summations of weighted inputs. In contrast, biological neurons utilize complex, three-dimensional dendritic trees to perform non-linear filtering and coincidence detection before a signal reaches the soma. This paper proposes a novel hardware architecture: the Silicon Dendrite Unit (SDU). By utilizing analog CMOS circuits to emulate dendritic capacitance and resistance, we demonstrate a hardware-level "Temporal Integration" that allows for 1,000x greater energy efficiency in processing time-series data compared to traditional GPUs. We provide a mathematical framework for this "Pal-Dendrite" model and discuss its implications for the future of Neuromorphic Computing.