ﺑﺮرﺳﯽ و ﻃﺮاﺣﯽ ﺳﺎﺧﺘﺎر ﻓﺸﺮدهﺳﺎزي ﺗﻘﺮﯾﺒﯽ ﺑﺮاي ﺣﺎﻓﻈﻪي ﻧﻬﺎن در ﮐﺎرﺑﺮدﻫﺎي ﺗﻘﺮﯾﺐ ﭘﺬﯾﺮ ﺑﺎ روﯾﮑﺮد زﻣﯿﻨﻪ وﯾﮋه (دادهﻫﺎي ﺗﺼﻮﯾﺮ)

Some driving technologies of the digital system design in the past dacades have faced a number of physical limits. Many modern applications like image and sig- nal processing, machine learning, data analytics, computer games, and AI do not really require full precision computing. Therefore, Approximate computing is con- sidered as a promising solution to cope with the dark silicon. Also, the emerging domain-specific approach to computer architecture can play the role in coping with practical limits of computing systems design. On the other hand, memory hierar- chy, especially cache memory plays the key role in computing systems. In addition, the cache compression can be defined as reducing the cache data size to achieve the benefits of a larger cache memory without suffering from the major drawbacks of a physically large cache architecture. According to the points mentioned, we address approximate cache compression for domain-specific (image data). The challenge of the design is the improvment of detecting patterns while satisfying high decompres- sion speed, simple structure, high compression ratio and output degradation within an acceptable range. We look at the problem from heuristic, analytic and physical implementation perspectives through analysis and circuit and system-level simula- tions. We improve the mainstream block compression approaches through approxi- mation and introduce the Image Approximate Block Compressor (IABC) algorithm; a simple, high block compression ratio and fast (one-cycle and two-cycles latency for decompression and compression, respectively) hardware compression algorithm. The porposed algorithm is examined through analysis, theoretical estimate of logic and time complexity, functional simualtion and HW synthesis and simultaion. The results are compared against one of the fastest multi-compressor algorithms and its approximate version, as our baselines. The eva‎luations reveal compression ratio of 25.7 on average (up to 106) against the baseline with an average ratio of 2.69 (up to 45.0) and the baseline approximate version with an average ratio of 2.7 (up to 45.2). The eva‎luation results also show that the compression benefits of IABC come at only 2.73% average error in the quality of a deep learning object recognition ap- plication. In addition, IABC generates high-quality outputs for stand-alone images with a 39.49dB average Peak Signal to Noise Ratio The mentioned qualities come at only 13% storage overhead. In addition, we look at the problem from the informa- tion theory perspective and show the effect of approximation on huffman encoding (which is used in some cache compression algorithms) targeting image data. The analysis and simulations show smaller huffman code lengths (2.18x) which leads to up to 2.48x block compression ratio.

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