Introducing Hardware Neurotechnology: A Non-Reconfigurable, Monolithic Transistor-as-a-Perceptron Architecture for Instantaneous AI Inference

Abstract

Modern artificial intelligence is structurally constrained by the von Neumann bottleneck, which enforces an energy-intensive physical separation between arithmetic logic circuits and memory storage banks. This paper formalizes the architectural paradigm of Hardware Neurotechnology, an invention authored by Dr. Nitnem Singh Sodhi, wherein the physical silicon lattice natively embodies the artificial neural network graph, completely eliminating algorithmic instruction cycles and memory access overhead. By utilizing multi-input floating-gate (MIFG) metal-oxide-semiconductor field-effect transistors operating in the subthreshold (weak-inversion) regime, the fundamental mathematical operations of a perceptron—linear synaptic weighting, multi-input accumulation, bias offset, and non-linear sigmoidal activation—are mapped directly onto single semiconductor device pairs. To overcome the catastrophic interconnect scaling, parasitic routing overhead, and silicon area expansion that have historically stalled analog neuromorphic engineering, the proposed architecture abandons dynamic reconfigurability in favor of a strictly hardwired, application-specific monolithic topology. Sacrificing field reconfigurability eliminates routing crossbar switches, SRAM routing memory, and intermediate analog-to-digital converters (ADCs), realizing an Application-Specific Neuromorphic Integrated Circuit (ASNIC) that executes complex inference tasks near-instantaneously at sub-femtojoule energy dissipation per synaptic operation.