Neural　network　quantization　has　become　an　important　research　area.　Deep　networks　run　with　low　precision　operations　at　inference　time　offer　power　and　space　advantages　over　high　precision　alternatives,　and　can　maintain　high　accuracy.　However,　few　quantization　can　demonstrate　this　advantage　on　hardware　platform,　because　the　design　of　quantization　algorithm　lacks　the　consideration　of　actual　hardware　implementation.　In　this　paper,　we　propose　an　efficient　quantization　method　for　hardware　implementation,　a　learnable　parameter　soft　clipping　fully　integer　quantization　(LSFQ),　which　includes　weight　quantization　and　activation　quantization　with　learnable　clipping　parameter　method.　The　quantization　parameters　are　optimized　automatically　by　back　propagation　to　minimize　the　loss,　then　the　BatchNorm　layer　and　convolutional　layer　are　fused,　and　the　bias　and　quantization　step　size　are　further　quantized.　In　this　way,　LSFQ　accomplishes　integer-only-arithmetic.　We　evaluate　the　quantization　algorithm　on　a　variety　of　models　including　VGG7,　mobile-net　v2　in　CIFAR10　and　CIFAR100.　The　results　show　that　when　the　quantization　reaches　3-bit　or　4-bit,　the　accuracy　loss　of　our　method　is　less　than　1%　compared　with　the　full-precision　network　In　addition,　we　design　an　accelerator　for　the　quantization　algorithm　and　deploy　it　to　the　FPGA　platform　to　verify　the　hardware-awareness　of　our　method.