Many computing-in-memory (CIM) macros achieve local inference with forward propagation (FP), and some CIM macros also support backward propagation (BP) computation. However, they can not calculate the weight change related to the learning rate and forward propagation input. these macros can not support backward propagation training algorithm completely. In this paper, we proposed a 28nm 64Kb SRAM based CIM macro, which supports a more complete backward propagation training algorithm. This macro supports three computing modes. A multiply unit (MU) supports FP and BP modes. A multiply circuit (MC) supports three-inputs-multiplication (TIM) mode for the weight change analog computing. MC uses the principle of charge sharing which has a high resistance to process variation and perfect linearity. In FP and BP modes, this macro achieves an energy efficiency of 42.1TOPS/W with 2-bit input, 8-bit weight and 14-bit output multiplication and accumulation operations (MAC). In TIM mode, this macro achieves an energy efficiency of 59.4 - 2222TOPS/W with multiplication of 3 inputs and 1 output.