We present the CSIMBA project in which we are developing a hyperspectral instrument for a nanosat (12U cubesat) IOD mission. It builds upon the results of the CHIEM initiative where we developed a novel compact imager with a hyperspectral sensor using thin film interference filters directly deposited on a 12Mpixel CMOS 2D detector array. The spectral range covers 475 to 900nm and has a resolution of approximately 5nm. The instrument provides a 80 km swath and 20 m GSD for a 500 km satellite orbit. The longer term goal is to build up a constellation of hyperspectral nanosats, capable of enhanced vegetation monitoring of agricultural fields and the biodiversity status with a revisit rate of a few days.

Recent advances in hyperspectral imaging techniques using spectral filters deposited directly onto an image sensor chip, and suitable for RPAS platforms, are reported in this paper. New filter configurations for a compact spectral camera are described and a prototype of the compact hyperspectral payload developed for small RPAS systems, as well as the first data acquired with the new camera are presented. The spectral range of the payload was optimized for earth observations such as vegetation monitoring or water quality studies. Although the spatially variable filters have to date only been used in small satellite sensors, this technology has a clear potential for RPAS platforms.

Reduced energy consumption is one of the most important design goals for embedded application domains like wireless communication, multimedia and biomedical applications. The instruction memory hierarchy has been proven to be one of the most power hungry parts of the system. This paper introduces an architectural enhancement for the instruction memory to reduce energy consumption and improve performance. The proposed distributed instruction memory organization requires minimal hardware overhead and supports the execution of multiple incompatible loops in parallel in a uni-processor system. We present different methods to implement the loop controller architecture, compare them, and show that distributing the instruction memory helps to reduce the interconnect cost as well. This architecture enhancement can reduce the energy consumed in the instruction memory hierarchy by 59% and improve the performance by 22% compared to hardware based enhanced SMT based architectures.

Modern mobile devices need to be extremely energy efficient. Due to the growing complexity of these devices, energy-aware design exploration has become increasingly important. Current exploration tools often do not support energy estimation, or require the design to be very detailed before estimation is possible. It is important to get early feedback on both performance and energy consumption during all phases of the design and at higher abstraction levels. This article presents a unified optimization and exploration framework to explore source-level transformation to processor architecture design space. The proposed retargetable compiler and simulator framework can map applications to a range of processors and memory configurations, simulate, and report detailed performance and energy estimates. An accurate and consistent energy modeling approach is introduced which can estimate the energy consumption of processor and memories at a component level, which can help to guide the design process. Fast energy-aware architecture exploration is illustrated by modeling both state-of-the-art processors as well as other architectures. Various design trade-offs are also illustrated on different academic as well as industrial benchmarks from both the wireless communication and multimedia domain. We also illustrate a design space exploration on different applications and show that there is large trade-off space between application performance, energy consumption, and area. We show that the proposed framework is consistent, accurate, and covers a large design space including various novel low-power extensions in a unified framework.

Modern portable embedded devices require processors that can provide sufficient performance for demanding multimedia and wireless applications. At the same time they have to be flexible to support a wide range of products and extremely energy efficient to provide a long battery life. Coarse grained reconfigurable architectures (CGRAs) potentially meet these constraints by providing a mix of flexible computational resources and large amounts of programmable interconnect. The vast design space of CGRAs complicates the development of optimized processors. Most effort has been spent on improving the performance. However, the energy cost of the programmable interconnect is becoming more expensive and this cost can no longer be neglected. In this work we present an energy- and performance-aware exploration for the interconnect of a CGRA and show that important tradeoffs can be made for those metrics. This will enable designers to develop more efficient architectures, tuned to a targeted application domain.

User demand, standards and products for digital nomadic communications are evolving quickly. The combination of this changing environment together with the need for short time-to-market pushes for more flexible implementations. Software Defined Radios (SDR) have been introduced as the ultimate way to achieve such flexibility. The reduced energy budget required by battery-powered solutions makes the typical worst-case static dimensioning unaffordable under highly dynamic operating conditions. Instead, more energy-scalable algorithms and implementations are entailed to provide flexibility while maintaining the required energy efficiency. Particularly, energy-scalable implementations can exploit data format properties to offer different tradeoffs between accuracy and energy. In this paper, such a technique is developed and applied to the SDR implementation of a 2 antennas 200 Mbps+ OFDM (Orthogonal Frequency-Division Multiplexing) inner modem receiver on a C-programmable CGA (Coarse Grain Array) processor with extensive SIMD (Single Instruction Multiple Data) support. By defining separate implementations for different combinations of modulation scheme and coding rate, up to 3-fold gains can be achieved in the average energy consumption.

In current embedded systems processors, multi-ported register files are one of the most power hungry parts of the processor, even when they are clustered. This paper presents a novel register file architecture, which has single ported cells and asymmetric interfaces to the memory and to the datapath. Several realistic kernels from the TI DSP benchmark and from software defined radio (SDR) are mapped on the architecture. A complete physical design of the architecture is done in TSMC 90nm technology. The novel architecture presented is shown to obtain energy gains of up to 10times with respect to conventional multi-ported register file over the different benchmarks

Reduced energy consumption is one of the most important design goals for embedded application domains like wireless, multimedia and biomedical. Instruction memory hierarchy has been proven to be one of the most power hungry parts of the system. This paper introduces an architectural enhancement for the instruction memory to reduce energy and improve performance. The proposed distributed instruction memory organization requires minimal hardware overhead and allows execution of multiple loops in parallel in a uni-processor system. This architecture enhancement can reduce the energy consumed in the instruction and data memory hierarchy by 70.01 % and improve the performance by 32.89% compared to enhanced SMT based architectures

Users expect future handheld devices to provide extended multimedia functionality and have long battery life. This type of application imposes heavy constraints on performance and power consumption and forces designers to optimize all parts of their platform. Evaluating the overall platform power breakdown is therefore critical to determine where to spend the efforts on power optimization. Surprisingly, few studies exist on that topic and decisions generally rely on common belief. We have realized a complete power breakdown for a realistic platform to identify the major power bottlenecks. This paper presents this power assessment of a realistic heterogeneous network on chip platform including processors, network and data/instruction memory hierarchy, running a video processing chain from camera to display. Our power breakdown identifies the main bottlenecks in the memory hierarchy and the foreground memory, and shows that global interconnect is not that critical for a well-optimized application mapping.

Users expect future handheld devices to provide extended multimedia functionality and have long battery life. This type of application imposes heavy constraints on both (realtime) performance and energy consumption and forces designers to optimise all parts of their platform. In this experiment we focus on the different processor core design options for embedded platforms, including the effect of instruction memory hierarchy on the energy consumption. The results show that significant improvements for energy efficiency and/or performance over currently used RISC or VLIW processors can be achieved. We conclude, based on concrete data for a realistic application, that different styles, including both configurable hardware and instruction set processors, find their way into heterogeneous platforms and designers need to be aware of the trade-offs. Secondly, we show for the same application task that a heavily optimised instruction/configuration memory hierarchy can significantly reduce the energy consumption of this part, so it forms a crucial part of every energy aware design.