Shrinking time-to-market and high demand for productivity has driven traditional hardware designers to use design methodologies that start from high-level languages. However, meeting timing constraints of automatically generated IPs is often a challenging and time-consuming task that must be repeated every time the specification is modified. To address this issue, a new generation of IP-design technologies that is capable of generating custom datapaths as well as programming an existing one is developed. These technologies are often based on Horizontal Microcoded Architectures. Large code size is a well-know problem in HMAs, and is referred to as "code bloating" problem.In this paper, we study the code size of one of the new HMAbased technologies called NISC. We show that NISC code size can be several times larger than a typical RISC processor, and we propose several low-overhead dictionary-based code compression techniques to reduce the code size. Our compression algorithm leverages the knowledge of "don't care" values in the control words to better compress the content of dictionary memories. Our experiments show that by selecting proper memory architectures the code size of NISC can be reduced by 70% (i.e. 3.3 times) at cost of only 9% performance degradation. We also show that some code compression techniques may increase number of utilized block RAMs in FPGA-based implementations. To address this issue, we propose combining dictionaries and implementing them using embedded dual-port memories.
Increasing demand for larger high-performance applications requires developing more complex systems with hundreds of processing cores on a single chip. To allow dynamic voltage scaling in each on-chip cores individually, many on-chip voltage regulators must be used. However, the limitations in implementation of onchip inductors can reduce the efficiency, accuracy and the number of voltage modes generated by regulators. Therefore the future voltage scheduling algorithms must be efficient, even in the presence of few voltage modes; and fast, in order to handle complex applications. Techniques proposed to date, need many fine-grained voltage modes to produce energy efficient results and their quality degrades significantly as the number of modes decreases. This paper presents a new technique called Adaptive Stochastic Gradient Voltage and Task Scheduling (ASG-VTS) that quickly generates very energy efficient results irrespective of the number of available voltage modes. The results of comparing our algorithm to the most efficient approaches (RVS and EE-GLSA) show that in the presence of only four valid modes, the ASG-VTS saves up to 26% and 33% more energy. On the other hand, other approaches require at least ten modes to reach the same level of energy saving that ASG-VTS achieves with only four modes. Therefore our algorithm can also be used to explore and minimize the number of required voltage levels in the system.
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