Name: Photonic Computing Architectures for Accelerated Artificial Intelligence. A Post-Silicon Paradigm for Energy-Efficient Neural Network Processing (en Inglés)
SKU: 67327836
Price: 760.06 MXN
Availability: InStock
Author: Laszlo Pokorny
ISBN: 9798197430755

portada Photonic Computing Architectures for Accelerated Artificial Intelligence. A Post-Silicon Paradigm for Energy-Efficient Neural Network Processing (en Inglés)

Photonic Computing Architectures for Accelerated Artificial Intelligence. A Post-Silicon Paradigm for Energy-Efficient Neural Network Processing (en Inglés)

Formato

Libro Físico

Editorial

Independently published

Autor

Laszlo Pokorny

Año

2026

Idioma

Inglés

N° páginas

278

Encuadernación

Tapa Blanda

Dimensiones

28x21.6x1.4 cm

ISBN13

9798197430755

Photonic Computing Architectures for Accelerated Artificial Intelligence. A Post-Silicon Paradigm for Energy-Efficient Neural Network Processing (en Inglés)

Laszlo Pokorny (Autor) · Independently published · Tapa Blanda

Libro Nuevo Importado

Envío: 14 a 19 días háb.

$ 1,266.77$ 760.06

-40%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan más de 100 unidades

$ 760.06

¡Envío Gratis! Llega entre el 28 Sep y el 06 Oct a CUAUHTÉMOC, Ciudad de México. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Photonic Computing Architectures for Accelerated Artificial Intelligence. A Post-Silicon Paradigm for Energy-Efficient Neural Network Processing (en Inglés)"

The exponential growth of artificial intelligence workloads has created an unsustainable trajectory in computational energy consumption, while the deceleration of Moore's Law and breakdown of Dennard scaling constrain electronic hardware from meeting this demand. This dissertation investigates photonic computing as a post-silicon paradigm for energy-efficient neural network processing, evaluating three architectures-Mach-Zehnder interferometer (MZI) mesh processors, micro-ring resonator (MRR) weight banks, and phase-change material (PCM) tensor cores-through a three-phase mixed-methods design. Phase 1 benchmarked four photonic against four electronic AI accelerators, revealing a median energy efficiency advantage of 2.36× (5.19 vs. 2.20 TOPS/W) with a large effect size (Cohen's d = 0.76) and directional support from 89.8% of 10,000 bootstrap resamples. Statistical significance was not achieved (p = .354) owing to 14.9% power in the near-census sample (n = 4 per group), consistent with a Type II error. Phase 2 modeled architectural barriers: silicon MRRs exhibited thermal drift of ~100 pm/°C, MZI processors lost >60% accuracy at ±3°C deviation, 64×64 meshes required phase error tolerances of σ ≤ 0.02 rad for 90% accuracy, and optical loss at 2.13 dB/stage constrained practical depth to ~100-150 stages. Phase 3 conducted multi-criteria decision analysis integrating technology readiness, investment, and patent data. PCM tensor cores ranked highest (6.72/10), exceeding MZI (5.65) and MRR (4.95) in a ranking robust under ±5% weight perturbation, supported by $2.164 billion in private investment and patent growth exceeding 30% CAGR. A five-layer sanity-check audit (8 PASS, 9 CAUTION, 3 FAIL) confirmed physical plausibility and internal consistency while flagging statistical power and outlier sensitivity as limitations. The evidence suggests photonic architectures offer substantial energy efficiency potential, with PCM designs providing the most promising long-term pathway contingent on thermal management and fabrication advances, while MZI processors represent the most viable near-term bridging technology.