XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference

Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. In this paper, we introduce the XNOR Neural Engine (XNE), a fully digital configurable hardware accelerator IP for BNNs, integrated within a microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid SRAM / standard cell memory. The XNE is able to fully compute convolutional and dense layers in autonomy or in cooperation with the core in the MCU to realize more complex behaviors. We show post-synthesis results in 65nm and 22nm technology for the XNE IP and post-layout results in 22nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6fJ per operation at 0.4V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies such as ResNet-34 in less than 2.2mJ per frame at 8.9 fps.Comment: 11 pages, 8 figures, 2 tables, 3 listings. Accepted for presentation at CODES'18 and for publication in IEEE Transactions on Computer-Aided Design of Circuits and Systems (TCAD) as part of the ESWEEK-TCAD special issu

A 24-GHz SiGe Phased-Array Receiver—LO Phase-Shifting Approach

A local-oscillator phase-shifting approach is introduced to implement a fully integrated 24-GHz phased-array receiver using an SiGe technology. Sixteen phases of the local oscillator are generated in one oscillator core, resulting in a raw beam-forming accuracy of 4 bits. These phases are distributed to all eight receiving paths of the array by a symmetric network. The appropriate phase for each path is selected using high-frequency analog multiplexers. The raw beam-steering resolution of the array is better than 10 [degrees] for a forward-looking angle, while the array spatial selectivity, without any amplitude correction, is better than 20 dB. The overall gain of the array is 61 dB, while the array improves the input signal-to-noise ratio by 9 dB

Studying co-running avionic real-time applications on multi-core COTS architectures

International audienceFor the last decades, industries from the safety-critical domain have been using Commercial Off-The-Shelf (COTS) architectures despite their inherent runtime variability. To guarantee hard real-time constraints in such systems, designers massively relied on resource over-provisioning and disabling the features responsible for runtime variability. The recent shift to multi-core architectures in the embedded COTS market worsened the runtime variability problem as contention on shared hardware resources brought new variability sources. Additionally, hiding this variability in additional safety margins as performed in the past will offset most if not all the multi-core performance gains. To enable the use of multi-cores in this domain, it has become essential to finely characterize at system level the application workload, as well as the possible contention on shared hardware resources. In this paper, we introduce measurement techniques based on a set of dedicated stressing benchmarks and architecture hardware monitors to characterize (1) the architecture, by identifying the shared hardware resources and their associated contention mechanisms. (2) the application, by identifying which shared hardware resources it is sensitive to. Such information would guide us toward identifying which applications can run smoothly together without endangering individual worst-case execution times

Design and application of reconfigurable circuits and systems

Open Acces

FPGAs in Industrial Control Applications

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs