Harnessing Photonics for Machine Intelligence

Abstract

The paper addresses how integrated photonics can accelerate AI workloads by leveraging optical bandwidth and parallelism. It moves beyond device-level innovations toward system-level analysis and full-stack design automation. A central theme involves "cross-layer co-design and workload-adaptive programmability to sustain high efficiency" across evolving applications at scale.

Key Contributions

• SimPhony simulation framework: A cross-layer tool modeling heterogeneous electronic-photonic systems from device to architecture, accounting for mixed-signal interfaces, memory traffic, and optical constraints — not just isolated optical metrics
• Bottleneck-driven taxonomy: Scaling analysis identifying critical dimensions (area, parallelism, precision, interface amortization) governing end-to-end efficiency
• Electronic-Photonic Design Automation (EPDA) roadmap: Spanning AI-assisted device simulation, circuit-level co-simulation, architecture-level modeling, inverse design, and layout automation
• Dynamic workload capability analysis: Showing Transformer attention mechanisms expose constraints that invalidate static CNN assumptions — MZI mesh architectures are "fundamentally ill-suited" for modern dynamic workloads
• PTC taxonomy: Three families — MZI mesh (coherent/static), weight-bank (incoherent), and time-multiplexed crossbar (dynamic-capable)

Methodology

• Cross-layer simulation via SimPhony evaluating full datapaths: optical cores, DAC/ADC converters, memory, and laser power
• Parameter sweeps across tensor core size, wavelength parallelism, bit precision, and operating frequency
• Dual workload comparison: static linear projections versus dynamic attention operations with matched MAC counts
• Inverse design survey covering optimization-driven (gradient-based, heuristic) and generative AI methods

Results

• Time-multiplexed crossbar "demonstrates a competitive position on the density-efficiency Pareto frontier relative to the NVIDIA A100" and surpasses B200 in energy efficiency
• MZI meshes and weight-bank designs show "substantial system-level overheads" on modern workloads
• DAC/ADC conversion consistently outweighs laser power and data movement as primary energy consumers
• "Brute-force scaling of electronic bit precision is unsustainable" — efficiency collapses beyond ~8-bit precision
• Inverse-designed components achieve "orders-of-magnitude smaller spatial footprint compared to manual counterparts"

Limitations

• Analog precision wall: High-resolution A/D conversion creates fundamental efficiency barriers; photonic platforms struggle with bit precision scaling beyond ~8 bits
• Workload rigidity: MZI mesh topology requires expensive SVD/phase decomposition at every token step — "token-rate timescales" make reconfiguration thermally and control-limited
• Sign representation overhead: Incoherent designs require decomposing signed operands, expanding to 4× hardware complexity for dynamic operations
• Fabrication-manufacturability gap: Inverse-designed components introduce sub-resolution features sensitive to process variation; robust fab-aware inverse design remains incomplete
• System tax dominance: Peripheral overheads (conversion, memory traffic, control) often exceed optical compute energy, limiting practical advantage at scale

Source: Harnessing Photonics for Machine Intelligence by Hanqing Zhu et al., University of Texas at Austin; Arizona State University