Stochastic Logic Realization of Matrix Operations
Stochastic Logic Realization of Matrix Operations
复制标题
矩阵运算的随机逻辑实现
DOI：
10.1109/dsd.2014.75
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
J. Hayes
中科院分区：
文献类型：
--
作者：
Pai;J. Hayes
随机计算（SC）是一种重新出现的技术，用于处理以数字比特流编码的概率数据。它的主要优点是算术运算可以用极小的低功耗逻辑电路来实现。这使得SC适合于涉及矩阵运算的信号处理应用，其VLSI实现是非常昂贵的。先前的SC方法仅解决具有相对低精度需求的基本矩阵运算。我们探讨使用SC实现一个代表性的复杂矩阵运算，即特征向量计算。我们将其应用于视觉人脸识别的训练任务，并表明我们的SC设计具有与传统的二进制对应物相当的性能，同时能够以计算时间换取准确性。
Stochastic computing (SC) is a re-emerging technique to process probability data encoded in digital bit-streams. Its main advantage is that arithmetic operations can be implemented by extremely small and low-power logic circuits. This makes SC suitable for signal-processing applications involving matrix operations whose VLSI implementation is very costly. Previous SC approaches only address basic matrix operations with relatively low accuracy needs. We explore the use of SC to implement a representative complex matrix operation, namely eigenvector computation. We apply it to a training task for visual face recognition, and show that our SC design has performance comparable to its conventional binary counterpart, while being able to trade computation time for accuracy.