FILM-QNN: Efficient FPGA Acceleration of Deep Neural Networks with Intra-Layer, Mixed-Precision Quantization
Bibliographic record
Abstract
With the trend to deploy Deep Neural Network (DNN) inference models on edge devices with limited resources, quantization techniques have been widely used to reduce on-chip storage and improve computation throughput. However, existing DNN quantization work deploying quantization below 8-bit may be either suffering from evident accuracy loss or facing a big gap between the theoretical improvement of computation throughput and the practical inference speedup. In this work, we propose a general framework, called FILM-QNN, to quantize and accelerate multiple DNN models across different embedded FPGA devices. First, we propose the novel intra-layer, mixed-precision quantization algorithm that assigns different precisions onto the filters of each layer. The candidate precision levels and assignment granularity are determined from our empirical study with the capability of preserving accuracy and improving hardware parallelism. Second, we apply multiple optimization techniques for the FPGA accelerator architecture in support of quantized computations, including DSP packing, weight reordering, and data packing, to enhance the overall throughput with the available resources. Moreover, a comprehensive resource model is developed to balance the allocation of FPGA computation resources (LUTs and DSPs) as well as data transfer and on-chip storage resources (BRAMs) to accelerate the computations in mixed precisions within each layer. Finally, to improve the portability of FILM-QNN, we implement it using Vivado High-Level Synthesis (HLS) on Xilinx PYNQ-Z2 and ZCU102 FPGA boards. Our experimental results of ResNet-18, ResNet-50, and MobileNet-V2 demonstrate that the implementations with intra-layer, mixed-precision (95% of 4-bit weights and 5% of 8-bit weights, and all 5-bit activations) can achieve comparable accuracy (70.47%, 77.25%, and 65.67% for the three models) as the 8-bit (and 32-bit) versions and comparable throughput (214.8 FPS, 109.1 FPS, and 537.9 FPS on ZCU102) as the 4-bit designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".