RESEARCH // 06 • HARDWARE & SILICON
From Intelligence in Software to Intelligence in Silicon.
Exploring the physical frontier of machine intelligence. We study analog circuits, continuous signal flow, and transistor physics to design non-von-Neumann architectures that compute with physics rather than clock cycles.
Discrete Symbolic Logic
Boolean gating, digital ALU cycles, synchronous clocks, and discrete memory bus transfers between CPU/GPU and DRAM.
Continuous Physical Computing
Kirchhoff current accumulation, sub-threshold conductance, continuous time state equations, and zero clock-skew memory.
Sub-Threshold MOSFET Conduction
Analyzing exponential current-voltage curves when operating transistors below their threshold voltage, enabling ultra-low-power neuromorphic weighting.
Analog Compute-in-Memory (CiM)
Directly performing matrix-vector multiplications within memory crossbar arrays, eliminating the massive energy cost of shuffling weights across buses.
EDA & Open-Source Silicon Toolchains
Developing automated SPICE netlist synthesis bridges that translate high-level agent policy definitions into verifiable hardware layout descriptions.
SCIENTIFIC TRANSPARENCY
Research & Development Status
Swadeshi AI Private Limited explicitly presents its semiconductor program as an exploratory R&D initiative. We do not claim commercial silicon production or active foundry tape-outs. Our current work focuses on mathematical modeling, transistor-level SPICE simulations, and theoretical circuit topologies.