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In this article, we describe the design choices behind MLPerf, a machine learning performance benchmark that has become an industry standard. The first two rounds of the MLPerf Training benchmark helped drive improvements to software-stack performance and scalability, showing a 1.3× speedup in the top 16-chip results despite higher quality targets and a 5.5× increase in system scale. The first round

The growing computational requirements of AI applications are challenging today's general-purpose CPU and GPU architectures and driving the need for purpose-built, programmable AI solutions. Habana Labs designed its Goya processor to meet the high throughput/low latency demands of Inference workloads, and its Gaudi processor for throughput combined with massive scale up and scale out capability needed

Tesla's full self-driving (FSD) computer is the world's first purpose-built computer for the highly demanding workloads of autonomous driving. It is based on a new System on a Chip (SoC) that integrates industry standard components such as CPUs, ISP, and GPU, together with our custom neural network accelerators. The FSD computer is capable of processing up to 2300 frames per second, a 21× improvement

NVIDIA's latest processor family, the Turing GPU, was designed to realize a vision for next-generation graphics combining rasterization, ray tracing, and deep learning. It includes fundamental advancements in several key areas: streaming multiprocessor efficiency, a Tensor Core for accelerated AI inferencing, and an RTCore for accelerated ray tracing. With these innovations, Turing unlocks both real-time

The “Zen 2” processor is designed to meet the needs of diverse markets spanning server, desktop, mobile, and workstation. The core delivers significant performance and energy-efficiency improvements over “Zen” by microarchitectural changes including a new TAGE branch predictor, a double-size op cache, and a double-width floating-point unit. Building upon the core design, a modular chiplet approach

Recent years have seen an explosion of demand for high-performance, high-efficiency compute available at scale. This demand has skyrocketed with the move to the public cloud and 5G networking, where compute nodes must operate within strict latency constraints and power budgets. The Neoverse N1 platform is Arm's latest high end offering from a scalable portfolio of IP for high performance and energy

In this article, we present TeraPHY, a monolithic electronic–photonic chiplet technology for low power and low latency, multi-Tb/s chip-to-chip communications. Integration of the TeraPHY optical technology with open source advanced interconnect bus interface enables communication between chips at board, rack, and row level at the energy and latency cost of in-package interconnect. This enables the

The number of endnodes in high-performance computing and datacenter systems is constantly increasing. Hence, it is crucial to minimize the impact of network congestion to guarantee a suitable network performance. InfiniBand is a prominent interconnect technology that allows implementing efficient topologies and routing algorithms, as well as queuing schemes that reduce the head-of-line (HoL) blocking

Multichiplet system-in-package designs have recently received a lot of attention as a mechanism to combat high SoC design costs and to economically manufacture large ASICs. These designs require low-power area-efficient off-die on-package die-to-die communication. Current technologies either extend on-die high-wire count buses using silicon interposers or off-package serial buses. The former approach

HPC architects are currently facing myriad challenges from ever tighter power constraints and changing workload characteristics. In this article, we discuss the current state of FPGAs within HPC systems. Recent technological advances show that they are well placed for penetration into the HPC market. However, there are still a number of research problems to overcome; we address the requirements for

Heterogeneous high-performance computing systems with GPUs are equipped with high-performance interconnects like InfiniBand, Omni-Path, PCIe, and NVLink. However, little exists in the literature that captures the performance impact of these interconnects on distributed deep learning (DL). In this article, we choose Horovod, a distributed training middleware, to analyze and profile various DNN training

Coupling regular topologies with optimized routing algorithms is key in pushing the performance of interconnection networks of supercomputers. In this article, we present Dmodc, a fast deterministic routing algorithm for parallel generalized fat trees (PGFTs), which minimizes congestion risk even under massive network degradation caused by equipment failure. Dmodc computes forwarding tables with a

This article presents the recent advancements on the Advanced IO Processor (AIOP), a network processor architecture designed by NXP Semiconductors. The AIOP is a multicore accelerated computing architecture where each core is equipped with dedicated hardware for rapid task switching on every hardware accelerator call. A hardware preemption controller snoops on the accelerator completions and sends

In this article, we present Warp, the first open hardware platform designed explicitly to support research in approximate computing. Warp incorporates 21 sensors, computation, and circuit-level facilities designed explicitly to enable approximate computing research, in a 3.6 cm × 3.3 cm × 0.5 cm device. Warp supports a wide range of precision and accuracy versus power and performance tradeoffs.

The enormous and ever-increasing complexity of state-of-the-art neural networks has impeded the deployment of deep learning on resource-limited embedded and mobile devices. To reduce the complexity of neural networks, this article presents $\Delta$ΔNN, a power-efficient architecture that leverages a combination of the approximate value locality of neuron weights and algorithmic structure of neural

Efficient deep learning inference requires algorithm and hardware codesign to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the neural architecture design makes the design space much larger: it is not only about designing the hardware architecture but also codesigning the neural architecture

In this article, the moving computation to data model (MC2D) is proposed to accelerate thread synchronization by pinning shared data to dedicated cores, and utilize in-hardware core-to-core messaging to communicate critical code execution. The MC2D model optimizes shared data locality by eliminating unnecessary data movement, and alleviates contended synchronization using nonblocking communication

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This special issue provides an overview of the foundational advances enabling 3-D monolithic systems, reports on exciting new advances, and identifies open opportunities and challenges.

The manufacturers of high-performance logic have been ardent champions of Moore's Law, which has resulted in exponential increase in aerial transistor density to 100 million transistors per square millimeter of silicon real estate. However, it is the memory chip makers who have taken the first step toward escaping the confines of scaling within the horizontal plane and have embraced the vertical or

The demands of future applications in computing (from self-driving cars to bioinformatics) overwhelm the projected capabilities of current electronic systems. The need to process unprecedented amounts of loosely structured data is driving the push for ultradense and fine-grained integration of traditionally off-chip components (e.g., sensors, memories) with energy-efficient computation units-all within

Computing-in-memory (CiM) is a popular design alternative to overcome the von Neumann bottleneck and improve the performance of artificial intelligence computing applications. Monolithic three-dimensional (M3D) technology is a promising solution to extend Moore's law through the development of CiM for data-intensive applications. In this article, we first discuss the motivation and challenges associated

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In recent years, the size of transistors has been scaled down to a few nanometers and further shrinking will eventually reach the atomic scale. Monolithic three-dimensional (M3D) ICs use the third dimension for placement and routing, which helps reduce footprint and improve power and performance of circuits without relying on technology shrinking. This article explores the benefits of M3D ICs using

Monolithic three-dimensional (M3D) integration is viewed as a promising improvement over through-silicon-via-based 3-D integration due to its greater inter-tier connectivity, higher circuit density, and lower parasitic capacitance. With M3D integration, network-on-chip (NoC) communication fabric can benefit from reduced link distances and improved intra-router efficiency. However, the sequential fabrication

Resistive RAM (RRAM) has been presented as a promising memory technology toward deep neural network (DNN) hardware design, with nonvolatility, high density, high ON/OFF ratio, and compatibility with logic process. However, prior RRAM works for DNNs have shown limitations on parallelism for in-memory computing, array efficiency with large peripheral circuits, multilevel analog operation, and demonstration

Nonvolatile memory, such as resistive RAM (ReRAM), is compatible with standard CMOS logic processes, allowing a sizable main memory system to be integrated into a CPU's die. ReRAM bitcells are fabricated within crosspoint subarrays that leave the bulk of transistors underneath the subarrays vacant. This permits placing the memory system over the CPU, improving area, parallelism, and power. Our work

The combination of specialized hardware and embedded nonvolatile memories (eNVM) holds promise for energy-efficient deep neural network (DNN) inference at the edge. However, integrating DNN hardware accelerators with eNVMs still presents several challenges. Multilevel programming is desirable for achieving maximal storage density on chip, but the stochastic nature of eNVM writes makes them prone to

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Economic analysis frames the debates in U.S. antitrust, and as such, it resembles an Italian Opera. While the best economists in the world sing in front of judges, most of the audience loses something in the translation. Without a guide, it is easy for the spectacle to distract. Observers miss crucial details, and lose the plot. Which motivates the topic today: How does antitrust economics inform a

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