• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.802-810
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• 1997

In this paper, the performance of direce mapping caches, 2_, 4_, 8_, .., 4096_way way set associative caches, and fully assiciative caches are analyized by trace simulation for verivying their effectiveness.In general, it is well known that as n, the number of main memory lines to be stored into one cache line number in direct mapping cache, increases, the performance of the cache memory should get higher linearly.According to our analysis, however, it is not true on all the cache organizations.It is shown that as n increases, miss ratios get lower only when the small cache(less than 256K) using large line size is used.It is also shown that fully associative mapping achieves high performance only when small size cache using large line size ia used.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.43.5-43
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• 2001

This paper considers a new synthesis of the optimally performing brain-state-in-a-box (BSB) neural associative memory given a set of prototype patterns to be stored as asymptotically stable equilibrium points with the large and uniform size of the domain of attraction (DOA). First, we propose a new theorem that will be used to provide a guideline in design of the BSB neural associative memory. Finally, a design example is given to illustrate the proposed approach and to compare with existing synthesis methods.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.936-939
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• 1996

In this paper, two specially designed associative mapping memories, called Associative Mapping Elements(AME) and Multiple-Digit Overlapping AME(MDO-AME), are presented for learning of nonlinear functions including kinematics and dynamics of robot manipulators. The proposed associative mapping memories consist of associative mapping rules(AMR) and weight update rules(WUR) which guarantee generalization and specialization of input-output relationship of learned nonlinear functions. Two simulation results, one for supervised learning and the other for unsupervised learning, are given to demonstrate the effectiveness of the proposed associative mapping memories.

• Proceedings of the Optical Society of Korea Conference
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• 1991.07a
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• pp.33-39
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• 1991

We have developed a new holographic associative memory(HAN) based on an adaptive learning which uses learning pattern method (LPM). The LPM utilizes the simple optical implementation of outer-product learning, performance of adapitive learning. simulation are represented.

• Proceedings of the Optical Society of Korea Conference
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• 1990.02a
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• pp.102-111
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• 1990

In this thesis, an optical bidirectional inner-product associative memory model using liquid crystal television is proposed and analyzed theoretically and realized experimentally. The LCTV is used as a SLM(spatial light modulator), which is more practical than conventional SLMs, to produce image vector in terms of computer and CCD camera. Memory and input vectors are recorded into each LCTV through the video input connectors of it by using the image board. Two multi-focus hololenses are constructed in order to perform optical inner-product process. In forward process, the analog values of inner-products are measured by photodetectors and are converted to digital values which are enable to control the weighting values of the stored vectors by changing the gray levels of the pixels of the LCTV. In backward process, changed stored vectors are used to produce output image vector which is used again for input vector after thresholding. After some iterations, one of the stored vectors is retrieved which is most similar to input vector in other words, has the nearest hamming distance. The experimental results show that the proposed inner-product associative memory model can be realized optically and coincide well with the computer simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.6
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• pp.353-359
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• 2016

In this paper, we propose a high performance L1 cache structure for the high clock CPU. The proposed cache memory consists of three parts, i.e., a direct-mapped cache to support fast access time, a two-way set associative buffer to reduce miss ratio, and a way-select table. The most recently accessed data is stored in the direct-mapped cache. If a data has a high probability of a repeated reference, when the data is replaced from the direct-mapped cache, the data is stored into the two-way set associative buffer. For the high performance and fast access time, we propose an one way among two ways set associative buffer is selectively accessed based on the way-select table (WST). According to simulation results, access time can be reduced by about 7% and 40% comparing with a direct cache and Intel i7-6700 with two times more space respectively.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.3
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• pp.125-131
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• 2018

STT-RAM is attracting as a next generation Non-volatile memory for replacing cache memory with low leakage energy, high integration and memory access performance similar to SRAM. However, there is problem of write operations as the other Non_volatile memory. Hybrid cache memory using SRAM and STT-RAM is attracting attention as a cache memory structure with lowe power consumption. Despite this, reducing the leakage energy consumption by the STT-RAM is still lacking access to the Dynamic energy. In this paper, we proposed as energy management method such as a way-selection approach for hybrid L2 cache fo SRAM and STT-RAM and memory selection method of write/read operation. According to the simulation results, the proposed hybrid cache memory reduced the average energy consumption by 40% on SPEC CPU 2006, compared with SRAM cache memory.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.449-456
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• 2004

This paper proposes a novel recognition model, a recurrent fuzzy associative memory(RFAM), for recognizing time-series patterns contained an ambiguity. RFAM is basically extended from FAM(Fuzzy Associative memory) by adding a recurrent layer which can be used to deal with sequential input patterns and to characterize their temporal relations. RFAM provides a Hebbian-style learning method which establishes the degree of association between input and output. The error back-propagation algorithm is also adopted to train the weights of the recurrent layer of RFAM. To evaluate the performance of the proposed model, we applied it to a word boundary detection problem of speech signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.154-162
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• 2003

In this paper, Associative Memory with Cellular Neural Networks classifies and recognizes image patterns as an operator applied to image process. CNN processes nonlinear data in real-time like neural networks, and made by cell which communicates with each other directly through its neighbor cells as the Cellular Automata does. It is applied to the optimization problem, associative memory, pattern recognition, and computer vision. Image processing with CNN is appropriate to 2-D images, because each cell which corresponds to each pixel in the image is simultaneously processed in parallel. This paper shows the method for designing the structure of associative memory based on CNN and getting output image by choosing the most appropriate weight pattern among the whole learned weight pattern memories. Each template represents weight values between cells and updates them by learning. Hebbian rule is used for learning template weights and LMS algorithm is used for classification.

• Proceedings of the Optical Society of Korea Conference
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• 1989.02a
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• pp.89-94
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• 1989

In this paper, we propose a hybrid optical/digital version of the associative memory which improve hardware efficiency and increase convergence rates. Multifocus hololens are used as space-varient optical element for performing inner product and summation function. The real-time input and the stored states of memory matrix is formated using LCTV. One method of adaptively changing the weights of stored vectors during each iteration is implemented electronically. A design for a optical implementation scheme is discussed and the proposed architecture is demonstrated the ability of retrieving with computer simmulation.