It is critical to deploy complicated neural network models on hardware with
limited resources. This paper proposes a novel model quantization method, named
the Low-Cost Proxy-Based Adaptive Mixed-Precision Model Quantization (LCPAQ),
which contains three key modules. The hardware-aware module is designed by
considering the hardware limitations, while an adaptive mixed-precision
quantization module is developed to evaluate the quantization sensitivity by
using the Hessian matrix and Pareto frontier techniques. Integer linear
programming is used to fine-tune the quantization across different layers. Then
the low-cost proxy neural architecture search module efficiently explores the
ideal quantization hyperparameters. Experiments on the ImageNet demonstrate
that the proposed LCPAQ achieves comparable or superior quantization accuracy
to existing mixed-precision models. Notably, LCPAQ achieves 1/200 of the search
time compared with existing methods, which provides a shortcut in practical
quantization use for resource-limited devices.

该论文提出了一种名为 LCPAQ 的新型模型量化方法，其中包含三个关键模块。在考虑硬件限制的基础上设计了硬件感知模块，开发了自适应混合精度量化模块来评估量化灵敏度，并使用希狄矩阵和 Pareto 边界技术进行整数线性规划来微调不同层的量化。然后，低成本代理神经架构搜索模块高效探索理想的量化超参数。在 ImageNet 上的实验证明，提出的 LCPAQ 在量化精度方面达到了与现有混合精度模型相当甚至更好的水平。值得注意的是，与现有方法相比，LCPAQ 的搜索时间减少了 1/200，从而为资源有限设备的实际量化使用提供了捷径。

基于低成本代理的混合精度自适应量化

Adaptive quantization with mixed-precision based on low-cost proxy

Current quantum annealing (QA) hardware suffers from practical limitations
such as finite temperature, sparse connectivity, small qubit numbers, and
control error. We propose new algorithms for mapping boolean constraint
satisfaction problems (CSPs) onto QA hardware mitigating these limitations. In
particular we develop a new embedding algorithm for mapping a CSP onto a
hardware Ising model with a fixed sparse set of interactions, and propose two
new decomposition algorithms for solving problems too large to map directly
into hardware.
The mapping technique is locally-structured, as hardware compatible Ising
models are generated for each problem constraint, and variables appearing in
different constraints are chained together using ferromagnetic couplings. In
contrast, global embedding techniques generate a hardware independent Ising
model for all the constraints, and then use a minor-embedding algorithm to
generate a hardware compatible Ising model. We give an example of a class of
CSPs for which the scaling performance of D-Wave's QA hardware using the local
mapping technique is significantly better than global embedding.
We validate the approach by applying D-Wave's hardware to circuit-based
fault-diagnosis. For circuits that embed directly, we find that the hardware is
typically able to find all solutions from a min-fault diagnosis set of size N
using 1000N samples, using an annealing rate that is 25 times faster than a
leading SAT-based sampling method. Further, we apply decomposition algorithms
to find min-cardinality faults for circuits that are up to 5 times larger than
can be solved directly on current hardware.

本研究提出了基于新型算法的量子退火硬件的布尔约束满足问题映射方法，可以缓解由于有限温度、稀疏连接、小量量子位和控制误差等限制所导致的问题。其中包括一种新的嵌码算法用于将 CSP 映射到硬件 Ising 模型，还提出了两种新的分解算法以用于解决直接映射到硬件不可行的问题，并使用 D-Wave 的硬件对基于电路的故障诊断方法进行了验证和应用。