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Hardware Acceleration of EDA Algorithms- P3: Single-threaded software applications have ceased to see significant gains in performance on a general-purpose CPU, even with further scaling in very large scale integration (VLSI) technology. This is a significant problem for electronic design automation (EDA) applications, since the design complexity of VLSI integrated circuits (ICs) is continuously growing. In this research monograph, we evaluate custom ICs, field-programmable gate arrays (FPGAs), and graphics processors as platforms for accelerating EDA algorithms, instead of the general-purpose singlethreaded CPU | 2.12 Applications 19 GPUs targeting scientific computations can handle IEEE double precision floating point 6 13 while providing peak performance as high as 900 Gflops. GPUs unlike FPGAs and custom ICs provide native support for floating point operations. 2.11 Security and Real-Time Applications In industry practice design details including HDL code are typically documented to make reuse more convenient. At the same time this makes IP piracy and infringement easier. It is estimated that the annual revenue loss due to IP infringement in the IC industry is in excess of 5 billion 42 . The goals of IP protection include enabling IP providers to protect their IPs against unauthorized use protecting all types of design data used to produce and deliver IPs and detecting and tracing the use of IPs 42 . FPGAs because of their re-programmability are becoming very popular for creating and exchanging VLSI IPs in the reuse-based design paradigm 27 . Existing watermarking and fingerprinting techniques embed identification information into FPGA designs to deter IP infringement. However such methods incur timing and or resource overheads and cause performance degradation. Custom ICs offer much better protection for intellectual property 33 . CPU GPU software IPs have higher IP protection risks. The emerging trend is that most IP exchange and reuse will be in the form of soft IPs because of the design flexibility they provide. The IP provider may also prefer to release soft IPs and leave the customer-dependent optimization process to the users 27 . From a security point of view protecting soft IPs is a much more challenging task than protecting hard IPs. Soft IPs are hard to trace and therefore not preferred in highly secure application scenarios. Compared to a CPU GPU-based implementation FPGA and custom IC designs are truly hard implementations. Software-based systems like CPUs and GPUs on the other hand often involve several layers of abstraction to schedule tasks and share .
