DEX: Digit-Level Early Exit for Energy-Efficient MSDF Neural Network Inference
Read the original on arXiv AI →The paper introduces DEX, an MSDF (most‑significant‑digit‑first) accelerator for quantized U‑Net brain‑tumor segmentation. It employs a two‑stage grouped processing element that supports signed INT8 operands and in‑stream bias accumulation, and implements four runtime mechanisms—exact early negative detection, exact sign‑only decision making, calibrated low‑order‑digit skipping, and calibrated pruning—to dynamically reduce computation. In experiments on a BraTS‑trained residual U‑Net, the approach cuts digit cycles by 38.38 % while maintaining a mean Dice score of 80.58 % (vs. 81.20 % for the floating‑point model), and the exact mechanisms alone achieve an 18.79 % cycle reduction without altering the quantized output.
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