首页> 外国专利> ACCELERATING NEURAL NETWORKS WITH LOW PRECISION-BASED MULTIPLICATION AND EXPLOITING SPARSITY IN HIGHER ORDER BITS

ACCELERATING NEURAL NETWORKS WITH LOW PRECISION-BASED MULTIPLICATION AND EXPLOITING SPARSITY IN HIGHER ORDER BITS

机译:加速基于精度的低精度的乘法和利用高阶位的稀疏性的神经网络

摘要

An apparatus to facilitate accelerating neural networks with low precision-based multiplication and exploiting sparsity in higher order bits is disclosed. The apparatus includes a processor comprising a re-encoder to re-encode a first input number of signed input numbers represented in a first precision format as part of a machine learning model, the first input number re-encoded into two signed input numbers of a second precision format, wherein the first precision format is a higher precision format than the second precision format. The processor further includes a multiply-add circuit to perform operations in the first precision format using the two signed input numbers of the second precision format; and a sparsity hardware circuit to reduce computing on zero values at the multiply-add circuit, wherein the processor to execute the machine learning model using the re-encoder, the multiply-add circuit, and the sparsity hardware circuit.
机译:公开了一种便于加速基于精度的基于低精度的乘法和利用高阶位的稀疏性的装置。 该装置包括包括重新编码器的处理器,以重新编码以第一精密格式表示的符号输入数量的第一输入数量,作为机器学习模型的一部分,将第一输入号重新编码成两个符号输入数。 第二精密格式,其中第一精密格式比第二精度格式更高的精度格式。 处理器还包括使用第二精度格式的两个符号输入数以第一精度格式执行操作的乘法电路; 和稀疏硬件电路,以减少乘法添加电路的零值上的计算,其中处理器使用重新编码器,乘法添加电路和稀疏硬件电路执行机器学习模型。

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