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【问题】
fckernel 是基于cl::Buffer实现，性能不佳softmax在处理二维tensor时，性能不佳，原因是并行度很低，比如维度为 1x1000 的 tensor，axis=1，只分配了一个线程来计算【本PR工作】
fc，input/output/bias 使用cl::Image2d存储，weight 使用cl::Buffer存储，且 weight 的读取方式是half16，具体参见 [OpenCL][Kernel] Use FC replace conv1x1 #6365 ；对应单测支持 fp32/fp16 两种精度验证softmax，针对处理二维tensor时性能不佳的问题，调整线程分配方式为对 axis 轴所在的数据以32进行分块处理，因此使用了 local memory，核心思想是并行 reduce；同时为了高效处理channel非4整除情况，使用mask来避免使用if/else判断【效果】
MobileNetV1 模型中有一个
fc和一个softmax，在包含 mali 和 adreno gpu 6 个设备上测试 kernel 耗时，如下表（耗时单位 ms）。fc可提速 1 ~ 3 倍，softmax可提速 44% ~ 302%单独在 845 上测试不同N值下的 FC 性能：
【TODO】
由于这两个 kernel 的输出都是 2 维的，当对其输出 tensor 的维度扩充为 4 维时，不是按照 opencl converter 中定义的对高维度pad 1，而是对低维度 pad 1，因此对 precision profile 会有影响，待解决此处。后续计划统一将 opencl converter 改为对低维度 pad 1。