Tianchen Wang

Efficient hardware implementation of cellular neural networks with powers-of-two based incremental quantization

Cellular neural networks (CeNNs) have been widely adopted in image processing tasks. Recently, various hardware implementations of CeNNs have emerged in the literature, with Field Programmable Gate Array (FPGA) being one of the most popular choices due to its high flexibility and low time-to-market. However, existing FPGA implementations of CeNNs are typically bounded by the limited number of embedded multipliers available therein, while the vast number of Logic Elements (LEs) and registers are never utilized. Apparently, such unbalanced resource utilization leads to sub-optimal CeNN performance and speed. To address this issue, in this paper we propose an incremental quantization based approach for the FPGA implementation of CeNNs. It quantizes the numbers in CeNN templates to powers of two, so that complex and expensive multiplications can be converted to simple and cheap shift operations, which only require a minimum number of registers and LEs. While similar concept has been explored in hardware implementations of Convolutional Neural Networks (CNNs), CeNNs have completely different computation patterns which require different quantization and implementation strategies. Experimental results on FPGAs show that our approach can significantly improve the resource utilization, and as a direct consequence a speedup up to 7.8x can be achieved with no performance loss compared with the state-of-the-art implementations. We also discover that different from CNNs, the optimal quantization strategies of CeNNs depend heavily on the applications. We hope that our work can serve as a pioneer in the hardware optimization of CeNNs.

Fast and accurate emissivity and absolute temperature maps measurement for integrated circuits

The comparison of temperatures (temperature correlation) obtained by measuring instruments and by thermal simulation is commonly necessary. Currently the way in which thermal maps are obtained by infrared thermographer yields inaccurate results since the emissivity values of all elements in an IC are ignored and measurement method assumes a constant emissivity. Without the correct settings of emissivity in infrared thermographer, the temperature variation could reach up to as high as 300 %. Coating black paint on the IC surface is a widely used method to assume the IC with constant emissivity and simplify the measurement procedures. Coating a uniform black thin film on an IC is a highly skillful technique and the coated black paint is un-removable. In certain cases, it is not convenient or possible to do so — for example, as monitoring a working chip. This article proposes the first practical and feasible method for emissivity map measurement. Two reference plates are utilized to obtain an emis-sivity map, from which real emissivity value of each pixel of the infrared thermographer is obtained. Firstly the radiances of IC and two reference plates are measured by the infrared thermographer. After that, the emissivity map of the IC can be calculated by the radiances. According to the experimental results herein, the uncertainty in the emissivity measured using this method is very low, of the order of 0.01, consistent with the minimum resolution of all currently available infrared thermographic instruments. With the emissivity map, the high accuracy temperature map is then obtained. The comparison of the temperature maps simulated by the extend version of Noxim (Access Noxim) as well as measured by the thermographer with constant emissivity and with the accurate emissivity map are presented in this article. This work contributes to the field of thermal analysis and simulation. Accurate circuit characteristics can be obtained through accurate thermal map; on the other hand, the closeness between the thermal simulation result and the real thermal map can also be realized.