  Transformers have excelled in many tasks including vision. However, efficient
deployment of transformer models in low-latency or high-throughput applications
is hindered by the computation in the attention mechanism which involves
expensive operations such as matrix multiplication and Softmax. To address
this, we introduce ReduceFormer, a family of models optimized for efficiency
with the spirit of attention. ReduceFormer leverages only simple operations
such as reduction and element-wise multiplication, leading to greatly
simplified architecture and improved inference performance, with up to 37%
reduction in latency and 44% improvement in throughput, while maintaining
competitive accuracy comparable to other recent methods. The proposed model
family is suitable for edge devices where compute resource and memory bandwidth
are limited, as well as for cloud computing where high throughput is sought
after.
