Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips

Two distinguishing features of state-of-the-art mobile and autonomous systems are 1) there are often multiple workloads, mainly deep neural network (DNN) inference, running concurrently and continuously; and 2) they operate on shared memory system-on-chips (SoC) that embed heterogeneous accelerators tailored for specific operations. State-of-the-art lacks efficient performance and resource management techniques necessary to either maximize total system throughput or minimize end-to-end workload latency. In this work, we propose HaX-CoNN, a novel scheme that characterizes and maps layers in concurrently executing DNN inference workloads to a diverse set of accelerators within a SoC. Our scheme uniquely takes per-layer execution characteristics, shared memory (SM) contention, and inter-accelerator transitions into account to find optimal schedules. We evaluate HaX-CoNN on NVIDIA Orin, NVIDIA Xavier, and Qualcomm Snapdragon 865 SoCs. Our experimental results indicate that HaX-CoNN minimizes memory contention by up to 45% and can improve latency and total throughput by up to 32% and 29%, respectively, compared to the state-of-the-art approaches

Automated Generation of Integrated Digital and Spiking Neuromorphic Machine Learning Accelerators

The growing numbers of application areas for artificial intelligence (AI) methods have led to an explosion in availability of domain-specific accelerators, which struggle to support every new machine learning (ML) algorithm advancement, clearly highlighting the need for a tool to quickly and automatically transition from algorithm definition to hardware implementation and explore the design space along a variety of SWaP (size, weight and Power) metrics. The software defined architectures (SODA) synthesizer implements a modular compiler-based infrastructure for the end-to-end generation of machine learning accelerators, from high-level frameworks to hardware description language. Neuromorphic computing, mimicking how the brain operates, promises to perform artificial intelligence tasks at efficiencies orders-of-magnitude higher than the current conventional tensor-processing based accelerators, as demonstrated by a variety of specialized designs leveraging Spiking Neural Networks (SNNs). Nevertheless, the mapping of an artificial neural network (ANN) to solutions supporting SNNs is still a non-trivial and very device-specific task, and completely lacks the possibility to design hybrid systems that integrate conventional and spiking neural models. In this paper, we discuss the design of such an integrated generator, leveraging the SODA Synthesizer framework and its modular structure. In particular, we present a new MLIR dialect in the SODA frontend that allows expressing spiking neural network concepts (e.g., spiking sequences, transformation, and manipulation) and we discuss how to enable the mapping of spiking neurons to the related specialized hardware (which could be generated through middle-end and backend layers of the SODA Synthesizer). We then discuss the opportunities for further integration offered by the hardware compilation infrastructure, providing a path towards the generation of complex hybrid artificial intelligence systems

Evaluation Of Outer Hair Cell Function And Medial Olivocochlear Efferent System In Patients With Type Ii Diabetes Mellitus

Aim: This study was designed to investigate the function of outer hair cells and medial olivocochlear efferents in type II diabetes mellitus (DM). Materials and methods: There were 50 patients with type II DM and 51 age-and sex-matched healthy controls included in the study. Both groups were compared in terms of transient evoked otoacoustic emissions (TEOAEs), distortion product otoacoustic emissions (DPOAEs), and contralateral suppression of TEOAE. Results: Pure tone thresholds of the patients with type II DM were significantly higher than in the controls (P < 0.05). The TEOAE amplitudes at 1 kHz and at 1.5, 2, 3, 4, and 6 kHz signal-to-noise ratio amplitudes on DPOAE testing were significantly lower in the patients than controls (P < 0.05). There was no significant difference between the type II DM and control groups regarding contralateral suppression test results of TEOAEs. Conclusion: Type II DM seems to impact the auditory system at the cochlear level by affecting the functions of outer hair cells, and it results in elevation of the thresholds on audiometry and a decrease in the amplitudes of otoacoustic emissions.WoSScopu