Physical design of dual-core system-on-chip

Two representative multimedia applications-AAC and H.264/AVC decoders on the parallel architecture core (PAC) SoC are introduced in the second part of the two introductory papers. The applications have been programmed on the PACDSP core and the PAC SoC to demonstrate the high-performance, low-power DSP computations and the effectiveness of the dynamic voltage and frequency scaling (DVFS) capability on the heterogeneous multicore SoC. First, techniques to exploit data-and instruction-level parallelisms existing in the application kernels are described for performance optimizations on the clustered VLIW architecture of PACDSP with the distributed register organization. Next, two variation techniques of asymmetric programming model are introduced by examples of decoders. Then, the energy efficiency of the programmable multimedia SoC is demonstrated using an innovative power-aware H.264/AVC decoder. Finally, a DVFS-aware framework for soft real-time video playback is provided by extending the power-aware decoding scheme. The work provides practical references of realizing multimedia applications on PAC SoC suitable for richfunction and resource constraint portable devices.

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Parallel Architecture Core (PAC)—the First Multicore Application Processor SoC in Taiwan Part II: Application Programming

Chen

Liu

Yang

et al. 2010

J Sign Process Syst

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Physical design method of MPSoC

Liu

Xia

Teng

2007

J. Zhejiang Univ. - Sci. A

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Harnessing logical reasoning ability is a comprehensive natural language understanding endeavor. With the release of Generative Pretrained Transformer 4 (GPT-4), highlighted as "advanced" at reasoning tasks, we are eager to learn the GPT-4 performance on various logical reasoning tasks. This report analyses multiple logical reasoning datasets, with popular benchmarks like LogiQA and ReClor, and newly-released datasets like AR-LSAT. We test the multi-choice reading comprehension and natural language inference tasks with benchmarks requiring logical reasoning. We further construct a logical reasoning out-ofdistribution dataset to investigate the robustness of ChatGPT and GPT-4. We also make a performance comparison between ChatGPT and GPT-4. Experiment results show that ChatGPT performs significantly better than the RoBERTa fine-tuning method on most logical reasoning benchmarks. With early access to the GPT-4 API we are able to conduct intense experiments on the GPT-4 model. The results show GPT-4 yields even higher performance on most logical reasoning datasets. Among benchmarks, ChatGPT and GPT-4 do relatively well on well-known datasets like LogiQA and ReClor. However, the performance drops significantly when handling newly released and out-of-distribution datasets. Logical reasoning remains challenging for ChatGPT and GPT-4, especially on outof-distribution and natural language inference datasets. We release the prompt-style logical reasoning datasets as a benchmark suite and name it LogiEval.

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Physical design of dual-core system-on-chip

Cited by 2 publications

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Parallel Architecture Core (PAC)—the First Multicore Application Processor SoC in Taiwan Part II: Application Programming

Parallel Architecture Core (PAC)—the First Multicore Application Processor SoC in Taiwan Part II: Application Programming

Physical design method of MPSoC

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