Recurrent Neural Networks (RNNs) are a key technology for applications such as automatic speech recognition or machine translation. Unlike conventional feed-forward DNNs, RNNs remember past information to improve the accuracy of future predictions and, therefore, they are very effective for sequence processing problems.

Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful RNN implementation, as they can learn long term dependencies to achieve high accuracy. Unfortunately, the recurrent nature of LSTM networks significantly constrains the amount of parallelism and, hence, multicore CPUs and many-core GPUs exhibit poor efficiency for RNN inference.

Dynamically typed programming languages have become very popular in recent years. These languages ease the task of programmers but introduce significant runtime overheads since variables are neither declared nor bound to a particular type. In particular, every time an object property is accessed, we have to obtain the type of the object to which it belongs just before the access, in order to properly compute the address of the property. This adds an important overhead to each of these accesses. In this paper, we present ERICO, a hybrid HW/SW mechanism that removes most of this overhead. This technique is based on a runtime-built structure that provides the information required to identify the addresses of object properties in a very efficient manner. Besides, a hardware cache of this structure stores its most frequently used elements to speedup its access. To demonstrate the effectiveness of this new mechanism, we implement it in a JavaScript engine and show that on average it produces 11% speedup (up to 31% for some applications) and 10.6% energy reduction.

Dynamically typed programming languages have become very popular in the recent years. These languages ease the task of the programmer but introduce significant overheads since assumptions about the types of variables have to be constantly validated at run time. Java Script is a widely used dynamically typed language that has gained significant popularity in recent years. In this paper, we provide a detailed analysis of the two main sources of overhead in the Java Script execution. The first one is the runtime overhead needed for dynamic compilation and housekeeping activities (i.e. Garbage collector, compilation, etc.). The second one is the additional checks and guards introduced by the dynamic nature of Java Script. Then, we propose three new HW/SW optimizations that reduce this latter type of overhead. We show that these two types of overhead represent 35% and 25% respectively of the total execution time on average for a representative workload, and the proposed optimizations provide a 6% average speedup.