Notes d’épisode
This episode examines ANNS-AMP, a June 2026 paper proposing adaptive mixed-precision computing for approximate nearest neighbor search. It starts with the IVF-PQ index pipeline: cluster locating, residual calculation, lookup-table construction, distance summation and top-k selection. The paper reports that over 99.9% of distance arithmetic in the cluster-locating and table-construction stages goes to vectors that are not neighbors. The proposed fix is a small runtime predictor that picks the bit-width for each cluster and each query, paired with a bit-serial, bit-plane near-memory accelerator so that fewer bits mean less compute and less memory traffic. The discussion also covers why existing index tuning and early-termination methods miss this redundancy. It questions whether flexible-precision hardware can succeed now that fixed INT8, FP8 and FP4 formats have taken over in neural networks. A running thread asks how much of the reported speed and energy gain comes from the precision idea and how much from the custom silicon.
Sources:
1. ANNS-AMP: Accelerating Approximate Nearest Neighbor Search via Adaptive Mixed-Precision Computing — Mingkai Chen, Cheng Liu, Shengwen Liang, Lei Zhang, Xiaowei Li, Huawei Li, 2026
http://arxiv.org/abs/2606.07156
2. Mixed Precision Training — Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, et al., 2018
https://scholar.google.com/scholar?q=Mixed+Precision+Training
3. HAQ: Hardware-Aware Automated Quantization with Mixed Precision — Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han, 2019
https://scholar.google.com/scholar?q=HAQ%3A+Hardware-Aware+Automated+Quantization+with+Mixed+Precision
4. HAWQ: Hessian Aware Quantization of Neural Networks with Mixed-Precision — Zhen Dong, Zhewei Yao, Amir Gholami, Michael Mahoney, Kurt Keutzer, 2019
https://scholar.google.com/scholar?q=HAWQ%3A+Hessian+Aware+Quantization+of+Neural+Networks+with+Mixed-Precision
5. Stripes: Bit-Serial Deep Neural Network Computing — Patrick Judd, Jorge Albericio, Tayler Hetherington, Tor Aamodt, Andreas Moshovos, 2016
https://scholar.google.com/scholar?q=Stripes%3A+Bit-Serial+Deep+Neural+Network+Computing
6. Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks — Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Vikas Chandra, Hadi Esmaeilzadeh, 2018
https://scholar.google.com/scholar?q=Bit+Fusion%3A+Bit-Level+Dynamically+Composable+Architecture+for+Accelerating+Deep+Neural+Networks
7. BISMO: A Scalable Bit-Serial Matrix Multiplication Overlay for Reconfigurable Computing — Yaman Umuroglu, Lahiru Rasnayake, Magnus Sjalander, 2018
https://scholar.google.com/scholar?q=BISMO%3A+A+Scalable+Bit-Serial+Matrix+Multiplication+Overlay+for+Reconfigurable+Computing
8. Product Quantization for Nearest Neighbor Search — Hervé Jégou, Matthijs Douze, Cordelia Schmid, 2011
https://scholar.google.com/scholar?q=Product+Quantization+for+Nearest+Neighbor+Search
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