प्रकाश से AI कंप्यूट
Photonic Processors, POLCERO की R&D दिशा है: केवल इलेक्ट्रॉनिक्स के बजाय प्रकाश का उपयोग करके हमारे रोबोटों में AI इन्फ़रेंस को तेज़ करना। ऑप्टिकल मैट्रिक्स गुणन - न्यूरल नेटवर्क में मूल संक्रिया - को एक फोटोनिक चिप से प्रकाश के एकल गुज़रान में किया जा सकता है, जो GPU/ASIC चिप्स की तुलना में उप-नैनोसेकंड विलंब और प्रति संक्रिया परिमाण की कोटियों में कम ऊर्जा प्रदान करता है।
R&D
R&D direction
A research direction: accelerating AI inference in our robots using light instead of electronics alone - lower power, higher throughput.
One optical pass vs thousands of clock cycles
Neural-network inference is dominated by matrix-vector multiplication (GEMM). Photonic chips perform it with light passing through Mach-Zehnder interferometer meshes - doing in a single optical pass what electronics need thousands of clock cycles to compute.
Optical math, electronic activations
Photonic chips excel at linear operations (matrix math) but not nonlinear ones (activation functions). The consensus architecture pairs optical cores for matrix math with electronic units for activations - a hybrid design taking the best of both.
A fast-growing accelerator market
The global photonic AI-accelerator market, 2025 to 2034 (CAGR 26.3%). As of 2026 we track 52 photonic-computing companies worldwide.
Swap the substrate, keep the intelligence
As photonic accelerators reach maturity (a 2027-2031 window for datacenter-class products), the POLCERO AI-brain architecture is built to absorb them - the abstraction layer lets us swap the compute substrate without retraining the task intelligence.
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