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In this paper, we describe the development of a platform‐based SoC of a 32‐bit smart card. The smart card uses a 32‐bit microprocessor for high performance and two cryptographic processors for high security. It supports both contact and contactless interfaces, which comply with ISO/IEC 7816 and 14496 Type B. It has a Java Card OS to support multiple applications. We modeled smart card readers with a foreign language interface for efficient verification of the smart card SoC. The SoC was implemented using 0.25 µm technology. To reduce the power consumption of the smart card SoC, we applied power optimization techniques, including clock gating. Experimental results show that the power consumption of the RSA and ECC cryptographic processors can be reduced by 32% and 62%, respectively, without increasing the area.

An application specific processor for an H.264 decoder with a configurable embedded processor is designed in this research. The motion compensation, inverse integer transform, inverse quantization, and entropy decoding algorithm of H.264 decoder software are optimized. We improved the performance of the processor with instruction‐level hardware optimization, which is tailored to configurable embedded processor architecture. The optimized instructions for video processing can be used in other video compression standards such as MPEG 1, 2, and 4. A significant performance improvement is achieved with high flexibility. Experimental results show that we could achieve 300% performance for the H.264 baseline profile level 2 decoder.

Ranging from circuit-level characterization to designing a platform architecture, developing a design automation tool, and fabricating a System on Chip (SoC), this article deals with the entire development process for ultralow-power (ULP) SoCs for Internet-of-Things (IoT) end nodes. More precisely, this article first focuses on the unique characteristics of the ULP circuits, the temperature effect inversion (TEI), i.e., the delay of the ULP circuits decreases with increasing temperature. Existing TEI-aware low-power (TEI-LP) techniques have incredible potential to further reduce the power consumption of conventional ULP SoCs, but there is a critical limitation to be widely adopted in real SoCs. To address this limitation and realize the ULP SoCs that can fully benefit from the TEI-LP techniques, this article proposes a new TEI-inspired SoC platform (TIP) architecture. On top of that, taking into account that the highly complex, time consuming, and labor-intensive development process of these ULP SoCs may hinder their widespread use for IoT end nodes, this article presents a new electronic design automation tool to accelerate ULP SoC development, RISC-V express (RVX). Finally, by using the RVX, this article introduces a TIP prototyping chip fabricated in 28-nm FD-SOI technology. This chip demonstrates that power savings of up to 35% can be achieved by lowering the supply voltage from 0.54 to 0.48 V at 25 • C and 0.44 V at 80 • C while continuing to operate at a target 50-MHz clock frequency.

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Abstract. Network-on-chip (NoC) is being proposed as a scalable and reusable communication platform for future SoC applications. The NoC, somewhat, resembles the parallel computer network. However, the NoC design highly requires the certain satisfaction of latency, power consumption, and area constraints. The latency of the network relates much to throughput and power consumption. Moreover, the IPs and the network are heterogeneous. Hence, a certain mapping of IPs onto a certain architecture produces a certain value of network latency as well as power consumption. The change of mapping scheme leads to a significant change of the values of these constraints. The fact that if we want to maximize the system's throughput, the network latency also increases and if we minimize the network latency, the trade off is that the throughput will decrease. In this paper, we present an mapping scheme that does compromise between throughput maximization and latency minimization. This sub-optimal mapping is found using the spanning tree searching algorithm. The experiment architecture using here is Mesh based topology. We use NS2 to simulate and calculate the system throughput and system power consumption is calculated using Orion model.

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