  Integer quantization of neural networks can be defined as the approximation
of the high precision computation of the canonical neural network formulation,
using reduced integer precision. It plays a significant role in the efficient
deployment and execution of machine learning (ML) systems, reducing memory
consumption and leveraging typically faster computations. In this work, we
present an integer-only quantization strategy for Long Short-Term Memory (LSTM)
neural network topologies, which themselves are the foundation of many
production ML systems. Our quantization strategy is accurate (e.g. works well
with quantization post-training), efficient and fast to execute (utilizing 8
bit integer weights and mostly 8 bit activations), and is able to target a
variety of hardware (by leveraging instructions sets available in common CPU
architectures, as well as available neural accelerators).
