NTX: An Energy-efficient Streaming Accelerator for Floating-point Generalized Reduction Workloads in 22 nm FD-SOI

Specialized coprocessors for Multiply-Accumulate (MAC) intensive workloads such as Deep Learning are becoming widespread in SoC platforms, from GPUs to mobile SoCs. In this paper we revisit NTX (an efficient accelerator developed for training Deep Neural Networks at scale) as a generalized MAC and reduction streaming engine. The architecture consists of a set of 32 bit floating-point streaming co-processors that are loosely coupled to a RISC-V core in charge of orchestrating data movement and computation. Post-layout results of a recent silicon implementation in 22 nm FD-SOI technology show the accelerator\u2019s capability to deliver up to 20 Gflop/s at 1.25 GHz and 168 mW. Based on these results we show that a version of NTX scaled down to 14 nm can achieve a 3 7 energy efficiency improvement over contemporary GPUs at 10.4 7 less silicon area, and a compute performance of 1.4 Tflop/s for training large state-of-the-art networks with full floating-point precision. An extended evaluation of MAC-intensive kernels shows that NTX can consistently achieve up to 87% of its peak performance across general reduction workloads beyond machine learning. Its modular architecture enables deployment at different scales ranging from high-performance GPU-class to low-power embedded scenario

A 0.80pJ/flop, 1.24Tflop/sW 8-to-64 bit Transprecision Floating-Point Unit for a 64 bit RISC-V Processor in 22nm FD-SOI

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 floating-point 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

A Scalable Near-Memory Architecture for Training Deep Neural Networks on Large In-Memory Datasets

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 x 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 x energy efficiency improvement of NTX over contemporary GPUs at 4.4 x 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 percent parallel and energy efficiency, while providing 2.1 x energy savings or 3.1 x performance improvement over a GPU-based system

Snitch: A Tiny Pseudo Dual-Issue Processor for Area and Energy Efficient Execution of Floating-Point Intensive Workloads

Data-parallel applications, such as data analytics, machine learning, and scientific computing, are placing an ever-growing demand on floating-point operations per second on emerging systems. With increasing integration density, the quest for energy efficiency becomes the number one design concern. While dedicated accelerators provide high energy efficiency, they are over-specialized and hard to adjust to algorithmic changes. We propose an architectural concept that tackles the issues of achieving extreme energy efficiency while still maintaining high flexibility as a general-purpose compute engine. The key idea is to pair a tiny 10kGE (kilo gate equivalent) control core, called Snitch, with a double-precision floating-point unit (FPU) to adjust the compute to control ratio. While traditionally minimizing non-floating-point unit (FPU) area and achieving high floating-point utilization has been a trade-off, with Snitch, we achieve them both, by enhancing the ISA with two minimally intrusive extensions: stream semantic registers (SSR) and a floating-point repetition instruction (FREP). SSRs allow the core to implicitly encode load/store instructions as register reads/writes, eliding many explicit memory instructions. The FREP extension decouples the floating-point and integer pipeline by sequencing instructions from a micro-loop buffer. These ISA extensions significantly reduce the pressure on the core and free it up for other tasks, making Snitch and FPU effectively dual-issue at a minimal incremental cost of 3.2 percent. The two low overhead ISA extensions make Snitch more flexible than a contemporary vector processor lane, achieving a $2 imes$2× energy-efficiency improvement. We have evaluated the proposed core and ISA extensions on an octa-core cluster in 22 nm technology. We achieve more than $6 imes$6× multi-core speed-up and a $3.5 imes$3.5× gain in energy efficiency on several parallel microkernels

A 4096-core RISC-V Chiplet Architecture for Ultra-efficient Floating-point Computing

Data-parallel problems demand ever growing floating-point (FP) operations per second under tight area-and energy-efficiency constraints. In this work we present Manticore, a general-purpose, ultra-efficient chiplet-based architecture for data-parallel FP workloads. We have manufactured a prototype of the chiplet's computational core in Globalfoundries 22FDX process and demonstrate more than 5x improvement in energy efficiency on FP intensive workloads compared to CPUs and GPUs. The compute capability at high energy and area efficiency is provided by Snitch clusters [1] containing eight small integer cores, each controlling a large floating-point unit (FPU). The core supports two custom ISA extensions: The Stream Semantic Register (SSR) extension elides explicit load and store instructions by encoding them as register reads and writes [2]. The Floating-point Repetition (FREP) extension decouples the integer core from the FPU allowing floating-point instructions to be issued independently. These two extensions allow the single-issue core to minimize its instruction fetch bandwidth and saturate the instruction bandwidth of the FPU, achieving FPU utilization above 90%, with more than 40% of core area dedicated to the FPU. © 2020 IEEE.ISSN:0272-1732ISSN:1937-414

Stream Semantic Registers: A Lightweight RISC-V ISA Extension Achieving Full Compute Utilization in Single-Issue Cores

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 percent 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 22 nm technology. Our extension provides a significant, 2x to 5x, architectural speedup across different kernels at a small 11 percent 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 percent 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

FPnew: An Open-Source Multiformat Floating-Point Unit Architecture for Energy-Proportional Transprecision Computing

The slowdown of Moore's law and the power wall necessitates a shift toward finely tunable precision (a.k.a.Transprecision) computing to reduce energy footprint. Hence, we need circuits capable of performing floating-point operations on a wide range of precisions with high energy proportionality. We present FPnew, a highly configurable open-source transprecision floating-point unit (TP-FPU), capable of supporting a wide range of standard and custom FP formats. To demonstrate the flexibility and efficiency of FPnew in general-purpose processor architectures, we extend the RISC-V ISA with operations on half-precision, bfloat16, and an 8-bit FP format, as well as SIMD vectors and multiformat operations. Integrated into a 32-bit RISC-V core, our TP-FPU can speedup the execution of mixed-precision applications by 1.67 imes with respect to an FP32 baseline, while maintaining end-To-end precision and reducing system energy by 37%. We also integrate FPnew into a 64-bit RISC-V core, supporting five FP formats on scalars or 2, 4, or 8-way SIMD vectors. For this core, we measured the silicon manufactured in Globalfoundries 22FDX technology across a wide voltage range from 0.45 to 1.2 V. The unit achieves leading-edge measured energy efficiencies between 178 Gflop/sW (on FP64) and 2.95 Tflop/sW (on 8-bit mini-floats), and a performance between 3.2 and 25.3 Gflop/s

Design of an open-source bridge between non-coherent burst-based and coherent cache-line-based memory systems

In heterogeneous computer architectures, the serial part of an application is coupled with domain-specific accelerators that promise high computing throughput and efficiency across a wide range of applications. In such systems, the serial part of a program is executed on a Central Processing Unit (CPU) core optimized for single-thread performance, while parallel sections are offloaded to Programmable Manycore Accelerators (PMCAs). This heterogeneity requires CPU cores and PMCAs to share data in memory efficiently, although CPUs rely on a coherent memory system where data is transferred in cache lines, while PMCAs are based on non-coherent scratchpad memories where data is transferred in bursts by DMA engines. In this paper, we tackle the challenges and hardware complexity of bridging the gap from a non-coherent, burst-based memory hierarchy to a coherent, cache-line-based one. We design and implement an open-source hardware module that reaches 97% peak throughput over a wide range of realistic linear algebra kernels and is suited for a wide spectrum of memory architectures. Implemented in a state-of-the-art 22 nm FD-SOI technology, our module bridges up to 650 Gbps at 130 fJ/bit and has a complexity of less than 1 kGE/Gbps

LLHD: A multi-level intermediate representation for hardware description languages

Modern Hardware Description Languages (HDLs) such as SystemVerilog or VHDL are, due to their sheer complexity, insufficient to transport designs through modern circuit design flows. Instead, each design automation tool lowers HDLs to its own Intermediate Representation (IR). These tools are monolithic and mostly proprietary, disagree in their implementation of HDLs, and while many redundant IRs exists, no IR today can be used through the entire circuit design flow. To solve this problem, we propose the LLHD multi-level IR. LLHD is designed as simple, unambiguous reference description of a digital circuit, yet fully captures existing HDLs. We show this with our reference compiler on designs as complex as full CPU cores. LLHD comes with lowering passes to a hardware-near structural IR, which readily integrates with existing tools. LLHD establishes the basis for innovation in HDLs and tools without redundant compilers or disjoint IRs. For instance, we implement an LLHD simulator that runs up to 2.4 7 faster than commercial simulators but produces equivalent, cycle-accurate results. An initial vertically-integrated research prototype is capable of representing all levels of the IR, implements lowering from the behavioural to the structural IR, and covers a sufficient subset of SystemVerilog to support a full CPU design

Indirection Stream Semantic Register Architecture for Efficient Sparse-Dense Linear Algebra

Sparse-dense linear algebra is crucial in many domains, but challenging to handle efficiently on CPUs, GPUs, and accelerators alike; multiplications with sparse formats like CSR and CSF require indirect memory lookups. In this work, we enhance a memory-streaming RISC-V ISA extension to accelerate sparse-dense products through streaming indirection. We present efficient dot, matrix-vector, and matrix-matrix product kernels using our hardware, enabling single-core FPU utilizations of up to 80% and speedups of up to 7.2x over an optimized baseline without extensions. A matrix-vector implementation on a multicore cluster is up to 5.8x faster and 2.7x more energy-efficient with our kernels than an optimized baseline. We propose further uses for our indirection hardware, such as scatter-gather operations and codebook decoding, and compare our work to state-of-the-art CPU, GPU, and accelerator approaches, measuring a 2.8x higher peak FP64 utilization in CSR matrix-vector multiplication than a GTX 1080 Ti GPU running a cuSPARSE kernel