Texas A&M University professor Daniel Jimenez has revolutionized the way research is conducted on microprocessors. He has developed many algorithms that enable the processor to predict whether a branch instruction will cause a change in the flow of control in a program, and most are based on neural learning.
Jimenez notes when he first started working on the idea of using neural learning, he found the timing constraints in a microprocessor would be a major obstacle. Jimenez says he spent several years "turning 'totally impractical' and 'almost impossible' into reality by inventing various techniques to solve the timing problem as well as improve accuracy." His branch predictors are among the most accurate in the industry and are used in a range of computing platforms.
The U.S. National Science Foundation has awarded Jimenez a CAREER grant for his research in this area.
His team in the Texas Architecture and Compiler Optimization lab has developed several dead-block prediction algorithms, as well as improving cache replacement policies for data hazards. Dead-block predictors give a prediction as to whether a block of data will be used again in the near future, which enables the cache controller to determine whether to keep a block or remove it in favor of a block that is more likely to be used soon, improving cache storage capacity and efficiency.
From Texas A&M Today
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