Architectural Blueprint the Spintronic Neural Processing Unit

This work presents the Spintronic Neural Processing Unit (sNPU), a non-von Neumann hardware architecture designed to eliminate the memory wall and thermal limitations of conventional silicon GPU architectures during large-scale artificial intelligence inference. The sNPU fuses deep learning execution and non-volatile weight storage into a single physical layer by leveraging a cobalt-stabilized Beta-Tungsten spin-injection heterostructure paired with a multi-domain CoFeB free layer. Matrix-vector multiplication is executed natively via Ohm's Law and Kirchhoff's Current Law across a dense SOT-MTJ crossbar array, achieving sub-nanosecond analog compute with zero static power leakage in the storage element. The specification addresses the four principal engineering challenges facing analog spintronic arrays: beta-W phase instability under BEOL thermal processing, stochastic domain wall write noise, Ovonic threshold switch snap-back during low-current readout, and ADC bandwidth constraints at the column periphery. Each challenge is mitigated through a dedicated circuit or materials solution detailed in the blueprint. A closed-loop write-verify programming architecture enforces deterministic multi-level conductance targeting across 16 to 256 stable analog weight plateaus, enabling 4-bit to 8-bit equivalent synaptic precision without relying on multi-oxide barrier stacks. A temporal bit-streaming pipeline bridges the physical 4–6 bit ADC resolution to INT8 logical inference precision. This blueprint is intended as a reference architecture for experimental fabrication efforts and as a contribution to the analog neuromorphic hardware design literature.