TRINE: A Token-Aware, Runtime-Adaptive FPGA Inference Engine for Multimodal AI

TRINE is a new FPGA accelerator and compiler that enables efficient end-to-end inference for multimodal AI models (combining vision transformers, CNNs, and language models) without requiring reconfiguration. The system achieves up to 22.57x latency reduction compared to RTX 4090 GPUs while consuming only 20-21W, demonstrating significant energy efficiency gains for embedded AI deployment.

TRINE addresses a critical bottleneck in edge AI: the challenge of running diverse neural network architectures on resource-constrained hardware. Multimodal AI stacks inherently suffer from computational inefficiency because vision transformers, convolutional networks, and graph neural networks have fundamentally different memory and compute access patterns. Traditional approaches require either multiple specialized accelerators or frequent reconfiguration, both of which increase latency and power consumption. TRINE solves this through a unified dataflow architecture that maps different layer types (DDMM, SDDMM, SpMM) onto a single reconfigurable processing array that dynamically switches execution modes at runtime.

The technical contributions extend beyond unified execution. Token pruning—selectively discarding low-importance tokens from transformer computations—yields up to 7.8x speedups on vision-heavy workloads by reducing downstream computation. Dependency-aware layer offloading orchestrates parallel execution across processing units, achieving 79% throughput improvements by eliminating idle cycles. These optimizations maintain sub-2.5% accuracy degradation with int8 quantization, a critical threshold for production deployments.

For the embedded AI market, TRINE's demonstrated performance—20-21W sustained power on Xilinx FPGAs while matching or exceeding GPU/edge accelerator latency—represents a compelling alternative for latency-sensitive, power-constrained applications like autonomous vehicles, robotics, and mobile inference. The single-bitstream deployment model eliminates reconfiguration overhead, critical for real-time systems with hard deadlines. This work signals growing viability of FPGAs as the preferred acceleration platform for heterogeneous workloads where energy efficiency and deterministic latency matter more than peak throughput.

• →TRINE enables unified multimodal inference on FPGAs with 22.57x latency improvement over RTX 4090 at 20-21W power consumption
• →Token pruning alone delivers up to 7.8x speedups for vision-transformer-heavy pipelines by eliminating low-importance computations
• →Dependency-aware layer offloading contributes 79% throughput improvement through intelligent scheduling across reconfigurable processing units
• →Int8 quantization maintains <2.5% accuracy loss across vision, language, and graph tasks, enabling production-grade deployment
• →Single-bitstream architecture eliminates reconfiguration overhead, critical for hard real-time embedded AI applications