What does this research mean for the field?

The Bosonic Helios Architecture (BHA) overcomes the von Neumann bottleneck and thermal limitations of traditional silicon AI accelerators by utilizing continuous-wave optical mechanics and plasmonic compute-in-memory for light-speed AI inference. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.ESTABLISHES_NEW_DIRECTION.

What question did this study set out to answer?

The main aim is to introduce the Bosonic Helios Architecture (BHA) as a solution to limitations in traditional silicon-based AI accelerators.

April 27, 2026Open Access

Bosonic Helios Architecture

Key Points

The main aim is to introduce the Bosonic Helios Architecture (BHA) as a solution to limitations in traditional silicon-based AI accelerators.
Describes LXIV wave mechanics for optical computation through wave superposition.
Outlines the use of plasmonic materials for compute-in-memory AI weight storage.
Details the integration of components like Gradient-Index Photonic Crystals for dynamic tensor operations.
The BHA demonstrates significantly improved thermal efficiency compared to classical silicon architectures.
Achieves zero-fetch, analog operations at a sub-diffraction scale for AI tensor computations.
Proposes a modular photonic network-on-chip with dynamic load balancing and defect calibration.

Abstract

This repository contains the foundational theoretical manuscripts for the Bosonic Helios Architecture (BHA), a novel macroscopic optical processor designed to natively accelerate artificial intelligence (AI) inference at the speed of light. As Large Language Models (LLMs) scale into the multi-trillion parameter regime, traditional silicon-based CMOS accelerators (GPUs and TPUs) face severe physical constraints—specifically the von Neumann memory bottleneck and unsustainable thermal degradation. The BHA addresses these limitations by fundamentally transitioning from discrete binary transistor logic to continuous-wave optical mechanics. Key architectural innovations detailed in this repository include: LXIV (64-State) Wave Mechanics: Computing natively via classical wave superposition across five orthogonal variables (Phase, Time, Frequency, Spatial Mode, Amplitude). This paradigm bypasses sequential boolean logic gates and completely circumvents the decoherence fragility inherent to single-photon quantum computing. Plasmonic Compute-In-Memory (CIM): Utilizing sub-50nm Silver (Ag) and Gold (Au) nanoantennas to store AI tensor weights as static physical geometry. This enables zero-fetch, analog Multiply-Accumulate (MAC) operations via Surface Plasmon Polaritons (SPPs) at a sub-diffraction scale. Volatile "Slow-Light" RAM: Employing Gradient-Index (GRIN) Photonic Crystals to achieve a room-temperature optical buffer. By collapsing the group velocity of the incoming wave, the BHA natively traps and preserves LXIV wave pulses for dynamic tensor activations. Extreme Thermal Stability: Fabricating the network on a Sapphire (Al2O3) substrate integrated with Silicon Nitride (Si3N4) waveguides, resulting in virtually zero localized Ohmic heat generation compared to standard silicon wafers. Scalable PNoC Topology: A modular Photonic Network-on-Chip managed by a custom software compiler (COP) that handles static defect calibration and dynamic spatial load balancing across Master and Standard Photonic Tensor Cores (PTCs). Included Manuscripts: Bosonic Helios Architecture (BHA): A Macroscopic Optical Tensor Core via LXIV Base-64 Continuous Wave Mechanics - Establishes the core continuous-wave physics, the hybrid plasmonic-dielectric CIM, and the operational mechanics of the X-Cube interference engine. Overcoming the von Neumann Bottleneck: A Comparative Architectural Analysis of the BHA vs. Contemporary Silicon AI Accelerators - A comparative evaluation of the BHA against modern digital architectures across compute bandwidth, thermal efficiency, I/O translation taxes, and solid-state data density. Correspondence & Contact: N. Abhay Kashyap: n.abhaykashyap@outlook.com Sreekumar kolachalam: sreekumarkolachalam@gmail.com

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Cite This Study

Kashyap et al. (Sun,) studied this question.

synapsesocial.com/papers/69eefd64fede9185760d406b https://doi.org/https://doi.org/10.5281/zenodo.19765006

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