—Reinforcement learning is a framework in which an agent can learn behavior without knowledge on a task or an environment by exploration and exploitation. Striking a balance betw...
Zhengqiao Ji, Q. M. Jonathan Wu, Maher A. Sid-Ahme...
Memory size reduction and memory accesses optimization are crucial issues for embedded systems. In the context of affine programs, these two challenges are classically tackled by ...
The memory subsystem accounts for a significant portion of the aggregate energy budget of contemporary embedded systems. Moreover, there exists a large potential for optimizing th...
Query expansion is a long-studied approach for improving retrieval effectiveness by enhancing the user's original query with additional related words. Current algorithms for ...
This paper evaluates several hardware-based data prefetching techniques from an energy perspective, and explores their energy/performance tradeoffs. We present detailed simulation...
Yao Guo, Saurabh Chheda, Israel Koren, C. Mani Kri...