In this paper, we present Ara, a 64-bit vector processor based on the version 0.5 draft of RISC-V's vector extension, implemented in GLOBALFOUNDRIES 22FDX FD-SOI technology. Ara's microarchitecture is scalable, as it is composed of a set of identical lanes, each containing part of the processor's vector register file and functional units. It achieves up to 97% FPU utilization when running a 256 × 256 double precision matrix multiplication on sixteen lanes. Ara runs at 1.2 GHz in the typical corner (TT/0.80 V/25 • C), achieving a performance up to 34 DP−GFLOPS. In terms of energy efficiency, Ara achieves up to 67 DP−GFLOPS/W under the same conditions, which is 56% higher than similar vector processors found in literature. An analysis on several vectorizable linear algebra computation kernels for a range of different matrix and vector sizes gives insight into performance limitations and bottlenecks for vector processors and outlines directions to maintain high energy efficiency even for small matrix sizes where the vector architecture achieves suboptimal utilization of the available FPUs.

Most investigations into near-memory hardware accelerators for deep neural networks have primarily focused on inference, while the potential of accelerating training has received relatively little attention so far. Based on an in-depth analysis of the key computational patterns in state-of-the-art gradient-based training methods, we propose an efficient near-memory acceleration engine called NTX that can be used to train state-of-the-art deep convolutional neural networks at scale. Our main contributions are: (i) a loose coupling of RISC-V cores and NTX co-processors reducing offloading overhead by 7× over previously published results; (ii) an optimized IEEE 754 compliant data path for fast high-precision convolutions and gradient propagation; (iii) evaluation of near-memory computing with NTX embedded into residual area on the Logic Base die of a Hybrid Memory Cube; and (iv) a scaling analysis to meshes of HMCs in a data center scenario. We demonstrate a 2.7× energy efficiency improvement of NTX over contemporary GPUs at 4.4× less silicon area, and a compute performance of 1.2 Tflop/s for training large state-of-the-art networks with full floating-point precision. At the data center scale, a mesh of NTX achieves above 95% parallel and energy efficiency, while providing 2.1× energy savings or 3.1× performance improvement over a GPU-based system.

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Single-issue processor cores are very energy efficient but suffer from the von Neumann bottleneck, in that they must explicitly fetch and issue the loads/storse necessary to feed their ALU/FPU. Each instruction spent on moving data is a cycle not spent on computation, limiting ALU/FPU utilization to 33% on reductions. We propose "Stream Semantic Registers" to boost utilization and increase energy efficiency. SSR is a lightweight, non-invasive RISC-V ISA extension which implicitly encodes memory accesses as register reads/writes, eliminating a large number of loads/stores. We implement the proposed extension in the RTL of an existing multi-core cluster and synthesize the design for a modern 22nm technology. Our extension provides a significant, 2x to 5x, architectural speedup across different kernels at a small 11% increase in core area. Sequential code runs 3x faster on a single core, and 3x fewer cores are needed in a cluster to achieve the same performance. The utilization increase to almost 100% in leads to a 2x energy efficiency improvement in a multi-core cluster. The extension reduces instruction fetches by up to 3.5x and instruction cache power consumption by up to 5.6x. Compilers can automatically map loop nests to SSRs, making the changes transparent to the programmer.

The crisis of Moore's law and new dominant Machine Learning workloads require a paradigm shift towards finely tunable-precision (a.k.a. transprecision) computing. More specifically, we need floating-point circuits that are capable to operate on many formats with high flexibility. We present the first silicon implementation of a 64-bit transprecision floatingpoint unit. It fully supports the standard double, single, and half precision, alongside custom bfloat and 8 bit formats. Operations occur on scalars or 2, 4, or 8-way SIMD vectors. We have integrated the 247 kGE unit into a 64 bit application-class RISC-V processor core, where the added transprecision support accounts for an energy and area overhead of merely 11% and 9%, respectively; yet achieving speedups and per-datum energy gains of 7.3x and 7.94x. We implemented the design in a 22 nm FD-SOI technology. The unit achieves energy efficiencies between 75 Gflop/sW and 1.24 Tflop/sW, and a performance between 1.85 Gflop/s and 14.83 Gflop/s, across formats.

The demand for floating-point compute power is ever growing. The domains of big-data, machine learning, and scientific computing require a wide precision range and high operational intensity. The sheer number of operations paired with increased power density implied by technology scaling makes it more important than ever to achieve maximum energyefficiency for floating point operations. In this work, we present Kosmodrom, our novel silicon solution in Globalfoundries 22 nm Fully-Depleted Silicon on Insulator (FD-SOI) which offers a multi-dimensional approach to trade-off performance, energyefficiency and power consumption. A variable-precision, dualcore RISC-V system together with a specialized floating point accelerator form the architectural basis. Different implementation strategies and standard cell flavors provide optimal solutions for different operating conditions while supply voltage and forward body bias (FBB) enable for a dynamic trade-off during operation. In this work, we provide a unique insight into the impact of a multitude of tuning parameters to achieve the optimal operating point on the power-performance surface. Kosmodrom achieves a peak energy-efficiency of 260Gflop/s/W and up to 28Gflop/s peak performance within a 6.2-400mW power envelope.