A Hardware-Friendly Alternative to Softmax Function and Its Efficient VLSI Implementation for Deep Learning Applications

The Softmax function holds an essential role in most machine learning algorithms. Conventional realization of Softmax necessitates computationally intensive exponential operations and divisions, thereby posing formidable challenges in developing low-cost hardware implementations. This paper presents a promising hardware-friendly alternative, Squaremax, which gets rid of complex exponential operations. The function definition is extremely simple and can thus be efficiently implemented in both software and hardware. Experimental results show that Squaremax consistently attains comparable or superior accuracy over several popular models. Besides, this paper also proposes an efficient hardware architecture design of Squaremax. It requires no functional units for exponential and logarithmic operations, and is even lookup table (LUT) free. It adopts a flexible 16-bit fixed-point Q format for I/O to better preserve the output precision, which leads to higher model accuracy. Moreover, it yields substantial improvements in speed, area, and power, as well as achieves remarkable area and power efficiency of 664 G/mm² and 1396 G/W in a 40nm process. Therefore, hardware-friendly Squaremax is a very promising alternative to complex Softmax in both software and hardware for deep learning applications, and the proposed hardware architecture design and efficient LUT-free implementation do achieve a notable improvement in speed, area, and power.