In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications. Still, both the energy efficiency and performance of such accelerators remain limited by memory accesses. In this paper, we focus on image applications, arguably the most important category among recognition and mining applications. The neural networks which are state-of-the-art for these applications are convolutional neural networks (CNNs), and they have an important property: weights are shared among many neurons, considerably reducing the neural network memory footprint. This property allows to entirely map a CNN within an SRAM, eliminating all DRAM accesses for weights. By further hoisting this accelerator next to the image sensor, it is possible to eliminate all remaining DRAM accesses, i.e., for inputs and outputs. In this paper, we propose such a CNN accelerator, placed next to a CMOS or CCD sensor. The absence of DRAM accesses combined with a careful exploitation of the specific data access patterns within CNNs allows us to design an accelerator which is highly energy-efficient. We present a single-core implementation down to the layout at 65 nm, with a modest footprint of 5.94mm$^{\boldsymbol {2}}$and consuming only 336mW, but still about$\boldsymbol {30\times }$faster than high-end GPUs. For visual processing with higher resolution and frame-rate requirements, we further present a multicore implementation with elevated performance.

In recent years, neural network accelerators have been shown to achieve both high energy efficiency and high performance for a broad application scope within the important category of recognition and mining applications.

Emerging 4G wireless communication systems need to deliver much higher data rates, more flexibility, and a significantly higher energy efficiency than current systems. To cope with this immense increase of requirements, new design approaches are a necessity. This paper focuses on the design of an advanced multiple-input-multiple-output (MIMO) detector, which is typically a bottleneck in the wireless receiver. In the proposed template-based design approach innovative architecture concepts, such as very wide register and distributed loop buffer, and algorithm-architecture co-optimizations are combined. The resulting MIMO detector processor, which is scalable to eight and more antennas, achieves a high area efficiency of 571 GOPS/mm2and a high energy efficiency of 3.3 GOPS/mW in the Taiwan Semiconductor Manufacturing Company (TSMC) 40-nm technology. By exploiting the dynamically varying requirements, the proposal has the potential to achieve a higher average energy efficiency than an application-specific integrated circuit (ASIC) equivalent. However, a penalty in total area consumption exists. The proposed architecture style offers an interesting and a very promising tradeoff in between the traditional ASIC and the other programmable processor solutions.

The reduction of the energy consumption in the domain of the embedded systems is becoming the most important design goal due to the increasing use of battery powered consumer devices. Previous research has pointed out the instruction memory organisation as one of the major sources of energy consumption of the embedded systems. Due to this fact, the introduction of any enhancement in this component of the system becomes crucial in order to decrease this energy bottleneck. The purpose of this paper is to present a highlevel energy analysis of the loop buffer schemes that exist in the embedded systems. The crucial energy analysis that is presented in this paper not only proposes a method to evaluate different loop buffer schemes for a certain application, but also guides embedded systems designers to make the correct decision in the trade-offs that exist between the energy budget, the required performance, and the area cost of the embedded system. Experimental results used in this analysis show that, the search of energy savings (up to 76%) has to take into account the performance penalty, the area cost, and the impact of the implementation technology in order to choose the most suitable enhancement that has to be introduced in the instruction memory organisation from the point of view of the energy consumption.

Advanced handheld applications are demanding for implementations of higher energy efficiency and higher performance. In typical implementations, the finite precision information is only known after fixed-point refinement, once the data-flow has been frozen. Instead, in this paper we suggest the propagation of finite precision information to drive data-flow transformations in order to achieve a higher mapping efficiency. Then, provided a flexible architecture with low run-time switching overhead, the data-flow under execution can opportunistically be tuned to provide the instantaneous computational accuracy required by the application. Thereby, the average number of operations and the precision of those is minimized. This principle is demonstrated with the implementation of the 128-point FFT present in a WLAN receiver. Compared to a conventional implementation, a reduction of 49% to 65% of the number of cycles can be achieved depending on conditions external to the receiver.

Emerging SDR baseband platforms are usually based on multiple DLP+ILP processors with massive parallelism. Although these platforms would theoretically enable advanced SDR signal processing, existing work implemented basic systems and simple algorithms. Importantly, MIMO is not fully supported in most implementations. Implemented MIMO but with a simple linear detector. Our work explores the feasibility for SDR implementations of soft-output ML MIMO detectors, which brings 6-12 dB SNR gains when compared to popular linear detectors. Although soft-output ML MIMO detectors are considered to be challenging even for ASICs, we combine architecture-friendly algorithms, application specific instructions, code transformations and ILP/DLP explorations to make SDR implementations feasible. In our work, a 2times4 ADRES based ASIP with 16-way SIMD can deliver 193 Mbps for 2times2 64 QAM, and 368 Mbps for 2times2 16 QAM transmissions. To the best of our knowledge, this is the first work exploring SDR based soft-output ML MIMO detectors.