arXiv subjects

Davide Rossi

Publications and source records attributed to Davide Rossi.

At least 37 records · Page 2Linked to original sources

arXiv2024

The Impact of the COVID-19 Pandemic on Women's Contribution to Public Code

Despite its promise of openness and inclusiveness, the development of free and open source software (FOSS) remains significantly unbalanced in terms of gender representation among contributors. To assist open source project maintainers and communities in addressing this imbalance, it is crucial to understand the causes of this inequality.In this study, we aim to establish how the COVID-19 pandemic has influenced the ability of women to contribute to public code. To do so, we use the Software Heritage archive, which holds the largest dataset of commits to public code, and the difference in differences (DID) methodology from econometrics that enables the derivation of causality from historical data.Our findings show that the COVID-19 pandemic has disproportionately impacted women's ability to contribute to the development of public code, relatively to men. Further, our observations of specific contributor subgroups indicate that COVID-19 particularly affected women hobbyists, identified using contribution patterns and email address domains.

cs.SE↗

arXiv2024

Culsans: An Efficient Snoop-based Coherency Unit for the CVA6 Open Source RISC-V application processor

Symmetric Multi-Processing (SMP) based on cache coherency is crucial for high-end embedded systems like automotive applications. RISC-V is gaining traction, and open-source hardware (OSH) platforms offer solutions to issues such as IP costs and vendor dependency. Existing multi-core cache-coherent RISC-V platforms are complex and not efficient for small embedded core clusters. We propose an open-source SystemVerilog implementation of a lightweight snoop-based cache-coherent cluster of Linux-capable CVA6 cores. Our design uses the MOESI protocol via the Arm's AMBA ACE protocol. Evaluated with Splash-3 benchmarks, our solution shows up to 32.87% faster performance in a dual-core setup and an average improvement of 15.8% over OpenPiton. Synthesized using GF 22nm FDSOI technology, the Cache Coherency Unit occupies only 1.6% of the system area.

eess.SY↗

arXiv2024

Spatzformer: An Efficient Reconfigurable Dual-Core RISC-V V Cluster for Mixed Scalar-Vector Workloads

Multi-core vector processor architectures excel in handling computationally intensive vectorizable tasks but struggle to achieve optimal resource utilization when facing sequential and control tasks that cannot be vectorized. This work presents Spatzformer, the first reconfigurable RISC-V V (RVV) architecture developed from a baseline open-source dual-core cluster based on Snitch scalar cores augmented with compact Spatz vector units. Spatzformer operates in two distinct modes: split mode, working as a dual-core vector architecture to handle vectorizable tasks concurrently, and merge mode, where two vector units are driven by a single scalar core, allowing the remaining scalar core to handle non-vectorizable control tasks. We implement Spatzformer in a 12-nm technology node and characterize the cost of the added architectural reconfigurability. We show that merge mode accelerates mixed scalar-vector kernels by up to 1.8x compared to split mode. Moreover, it accelerates the vector kernels that require fine-grained synchronization (such as FFT) by up to 20% with respect to the baseline. The reconfigurability features do not degrade the architecture's maximum frequency (1.2GHz, TT, 0.8V, 25C) and have a negligible area impact (+1.4%), with a worst-case energy efficiency drop of only 7% with respect to the non-reconfigurable baseline.

cs.AR↗

arXiv2024

Occamy: A 432-Core 28.1 DP-GFLOP/s/W 83% FPU Utilization Dual-Chiplet, Dual-HBM2E RISC-V-based Accelerator for Stencil and Sparse Linear Algebra Computations with 8-to-64-bit Floating-Point Support in 12nm FinFET

We present Occamy, a 432-core RISC-V dual-chiplet 2.5D system for efficient sparse linear algebra and stencil computations on FP64 and narrow (32-, 16-, 8-bit) SIMD FP data. Occamy features 48 clusters of RISC-V cores with custom extensions, two 64-bit host cores, and a latency-tolerant multi-chiplet interconnect and memory system with 32 GiB of HBM2E. It achieves leading-edge utilization on stencils (83 %), sparse-dense (42 %), and sparse-sparse (49 %) matrix multiply.

cs.AR↗

arXiv2024

Unleashing OpenTitan's Potential: a Silicon-Ready Embedded Secure Element for Root of Trust and Cryptographic Offloading

The rapid advancement and exploration of open-hardware RISC-V platforms are driving significant changes in sectors like autonomous vehicles, smart-city infrastructure, and medical devices. OpenTitan stands out as a groundbreaking open-source RISC-V design with a comprehensive security toolkit as a standalone system-on-chip (SoC). OpenTitan includes Earl Grey, a fully implemented and silicon-proven SoC, and Darjeeling, announced but not yet fully implemented. Earl Grey targets standalone SoC implementations, while Darjeeling is for integrable implementations. The literature lacks a silicon-ready embedded implementation of an open-source Root of Trust, despite lowRISC's efforts on Darjeeling. We address the limitations of existing implementations by optimizing data transfer latency between memory and cryptographic accelerators to prevent under-utilization and ensure efficient task acceleration. Our contributions include a comprehensive methodology for integrating custom extensions and IPs into the Earl Grey architecture, architectural enhancements for system-level integration, support for varied boot modes, and improved data movement across the platform. These advancements facilitate deploying OpenTitan in broader SoCs, even without specific technology-dependent IPs, providing a deployment-ready research vehicle for the community. We integrated the extended Earl Grey architecture into a reference architecture in a 22nm FDX technology node, benchmarking the enhanced architecture's performance. The results show significant improvements in cryptographic processing speed, achieving up to 2.7x speedup for SHA-256/HMAC and 1.6x for AES accelerators compared to the baseline Earl Grey architecture.

eess.SY↗

arXiv2024

A Gigabit, DMA-enhanced Open-Source Ethernet Controller for Mixed-Criticality Systems

The ongoing revolution in application domains targeting autonomous navigation, first and foremost automotive "zonalization", has increased the importance of certain off-chip communication interfaces, particularly Ethernet. The latter will play an essential role in next-generation vehicle architectures as the backbone connecting simultaneously and instantaneously the zonal/domain controllers. There is thereby an incumbent need to introduce a performant Ethernet controller in the open-source HW community, to be used as a proxy for architectural explorations and prototyping of mixed-criticality systems (MCSs). Driven by this trend, in this work, we propose a fully open-source, DMA-enhanced, technology-agnostic Gigabit Ethernet architecture that overcomes the limitations of existing open-source architectures, such as Lowrisc's Ethernet, often tied to FPGA implementation, performance-bound by sub-optimal design choices such as large memory buffers, and in general not mature enough to bridge the gap between academia and industry. Besides the area advantage, the proposed design increases packet transmission speed up to almost 3x compared to Lowrisc's and is validated through implementation and FPGA prototyping into two open-source, heterogeneous MCSs.

cs.AR↗

arXiv2024

TOP: Towards Open & Predictable Heterogeneous SoCs

Ensuring predictability in modern real-time Systems-on-Chip (SoCs) is an increasingly critical concern for many application domains such as automotive, robotics, and industrial automation. An effective approach involves the modeling and development of hardware components, such as interconnects and shared memory resources, to evaluate or enforce their deterministic behavior. Unfortunately, these IPs are often closed-source, and these studies are limited to the single modules that must later be integrated with third-party IPs in more complex SoCs, hindering the precision and scope of modeling and compromising the overall predictability. With the coming-of-age of open-source instruction set architectures (RISC-V) and hardware, major opportunities for changing this status quo are emerging. This study introduces an innovative methodology for modeling and analyzing State-of-the-Art (SoA) open-source SoCs for low-power cyber-physical systems. Our approach models and analyzes the entire set of open-source IPs within these SoCs and then provides a comprehensive analysis of the entire architecture. We validate this methodology on a sample heterogenous low-power RISC-V architecture through RTL simulation and FPGA implementation, minimizing pessimism in bounding the service time of transactions crossing the architecture between 28% and 1%, which is considerably lower when compared to similar SoA works.

cs.AR↗

arXiv2024

Siracusa: A 16 nm Heterogenous RISC-V SoC for Extended Reality with At-MRAM Neural Engine

Extended reality (XR) applications are Machine Learning (ML)-intensive, featuring deep neural networks (DNNs) with millions of weights, tightly latency-bound (10-20 ms end-to-end), and power-constrained (low tens of mW average power). While ML performance and efficiency can be achieved by introducing neural engines within low-power systems-on-chip (SoCs), system-level power for nontrivial DNNs depends strongly on the energy of non-volatile memory (NVM) access for network weights. This work introduces Siracusa, a near-sensor heterogeneous SoC for next-generation XR devices manufactured in 16 nm CMOS. Siracusa couples an octa-core cluster of RISC-V digital signal processing cores with a novel tightly-coupled "At-Memory" integration between a state-of-the-art digital neural engine called N-EUREKA and an on-chip NVM based on magnetoresistive memory(MRAM), achieving 1.7x higher throughput and 3x better energy efficiency than XR SoCs using NVM as background memory. The fabricated SoC prototype achieves an area efficiency of 65.2 GOp/s/mm2 and a peak energy efficiency of 8.84 TOp/J for DNN inference while supporting complex heterogeneous application workloads, which combine ML with conventional signal processing and control.

cs.AR↗

arXiv2024

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads.

cs.AR↗

arXiv2024

Assessing the Performance of OpenTitan as Cryptographic Accelerator in Secure Open-Hardware System-on-Chips

RISC-V open-source systems are emerging in deployment scenarios where safety and security are critical. OpenTitan is an open-source silicon root-of-trust designed to be deployed in a wide range of systems, from high-end to deeply embedded secure environments. Despite the availability of various cryptographic hardware accelerators that make OpenTitan suitable for offloading cryptographic workloads from the main processor, there has been no accurate and quantitative establishment of the benefits derived from using OpenTitan as a secure accelerator. This paper addresses this gap by thoroughly analysing strengths and inefficiencies when offloading cryptographic workloads to OpenTitan. The focus is on three key IPs - HMAC, AES, and OpenTitan Big Number accelerator (OTBN) - which can accelerate four security workloads: Secure Hash Functions, Message Authentication Codes, Symmetric cryptography, and Asymmetric cryptography. For every workload, we develop a bare-metal driver for the OpenTitan accelerator and analyze its efficiency when computation is offloaded from a RISC-V application core within a System-on-Chip designed for secure Cyber-Physical Systems applications. Finally, we assess it against a software implementation on the application core. The characterization was conducted on a cycle-accurate RTL simulator of the System-on-Chip (SoC). Our study demonstrates that OpenTitan significantly outperforms software implementations, with speedups ranging from 4.3x to 12.5x. However, there is potential for even greater gains as the current OpenTitan utilizes a fraction of the accelerator bandwidths, which ranges from 16% to 61%, depending on the memory being accessed and the accelerator used. Our results open the way to the optimization of OpenTitan-based secure platforms, providing design guidelines to unlock the full potential of its accelerators in secure applications.

cs.CR↗

arXiv2024

A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation

The rapid advancement of energy-efficient parallel ultra-low-power (ULP) ucontrollers units (MCUs) is enabling the development of autonomous nano-sized unmanned aerial vehicles (nano-UAVs). These sub-10cm drones represent the next generation of unobtrusive robotic helpers and ubiquitous smart sensors. However, nano-UAVs face significant power and payload constraints while requiring advanced computing capabilities akin to standard drones, including real-time Machine Learning (ML) performance and the safe co-existence of general-purpose and real-time OSs. Although some advanced parallel ULP MCUs offer the necessary ML computing capabilities within the prescribed power limits, they rely on small main memories (<1MB) and ucontroller-class CPUs with no virtualization or security features, and hence only support simple bare-metal runtimes. In this work, we present Shaheen, a 9mm2 200mW SoC implemented in 22nm FDX technology. Differently from state-of-the-art MCUs, Shaheen integrates a Linux-capable RV64 core, compliant with the v1.0 ratified Hypervisor extension and equipped with timing channel protection, along with a low-cost and low-power memory controller exposing up to 512MB of off-chip low-cost low-power HyperRAM directly to the CPU. At the same time, it integrates a fully programmable energy- and area-efficient multi-core cluster of RV32 cores optimized for general-purpose DSP as well as reduced- and mixed-precision ML. To the best of the authors' knowledge, it is the first silicon prototype of a ULP SoC coupling the RV64 and RV32 cores in a heterogeneous host+accelerator architecture fully based on the RISC-V ISA. We demonstrate the capabilities of the proposed SoC on a wide range of benchmarks relevant to nano-UAV applications. The cluster can deliver up to 90GOp/s and up to 1.8TOp/s/W on 2-bit integer kernels and up to 7.9GFLOp/s and up to 150GFLOp/s/W on 16-bit FP kernels.

cs.AR↗

arXiv2024

TitanCFI: Toward Enforcing Control-Flow Integrity in the Root-of-Trust

Modern RISC-V platforms control and monitor security-critical systems such as industrial controllers and autonomous vehicles. While these platforms feature a Root-of-Trust (RoT) to store authentication secrets and enable secure boot technologies, they often lack Control-Flow Integrity (CFI) enforcement and are vulnerable to cyber-attacks which divert the control flow of an application to trigger malicious behaviours. Recent techniques to enforce CFI in RISC-V systems include ISA modifications or custom hardware IPs, all requiring ad-hoc binary toolchains or design of CFI primitives in hardware. This paper proposes TitanCFI, a novel approach to enforce CFI in the RoT. TitanCFI modifies the commit stage of the protected core to stream control flow instructions to the RoT and it integrates the CFI enforcement policy in the RoT firmware. Our approach enables maximum reuse of the hardware resource present in the System-on-Chip (SoC), and it avoids the design of custom IPs and the modification of the compilation toolchain, while exploiting the RoT tamper-proof storage and cryptographic accelerators to secure CFI metadata. We implemented the proposed architecture on a modern RISC-V SoC along with a return address protection policy in the RoT, and benchmarked area and runtime overhead. Experimental results show that TitanCFI achieves overhead comparable to SoA hardware CFI solutions for most benchmarks, with lower area overhead, resulting in 1% of additional area occupation.

cs.CR↗

arXiv2023

Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing

Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and nano-robotics need to run many diverse tasks within a power envelope of a few tens of mW over a wide range of operating conditions: compute-intensive but strongly quantized Deep Neural Network (DNN) inference, as well as signal processing and control requiring high-precision floating-point. We present Marsellus, an all-digital heterogeneous SoC for AI-IoT end-nodes fabricated in GlobalFoundries 22nm FDX that combines 1) a general-purpose cluster of 16 RISC-V Digital Signal Processing (DSP) cores attuned for the execution of a diverse range of workloads exploiting 4-bit and 2-bit arithmetic extensions (XpulpNN), combined with fused MAC&LOAD operations and floating-point support; 2) a 2-8bit Reconfigurable Binary Engine (RBE) to accelerate 3x3 and 1x1 (pointwise) convolutions in DNNs; 3) a set of On-Chip Monitoring (OCM) blocks connected to an Adaptive Body Biasing (ABB) generator and a hardware control loop, enabling on-the-fly adaptation of transistor threshold voltages. Marsellus achieves up to 180 Gop/s or 3.32 Top/s/W on 2-bit precision arithmetic in software, and up to 637 Gop/s or 12.4 Top/s/W on hardware-accelerated DNN layers.

cs.AR↗

arXiv2023

Hybrid Modular Redundancy: Exploring Modular Redundancy Approaches in RISC-V Multi-Core Computing Clusters for Reliable Processing in Space

Space Cyber-Physical Systems (S-CPS) such as spacecraft and satellites strongly rely on the reliability of onboard computers to guarantee the success of their missions. Relying solely on radiation-hardened technologies is extremely expensive, and developing inflexible architectural and microarchitectural modifications to introduce modular redundancy within a system leads to significant area increase and performance degradation. To mitigate the overheads of traditional radiation hardening and modular redundancy approaches, we present a novel Hybrid Modular Redundancy (HMR) approach, a redundancy scheme that features a cluster of RISC-V processors with a flexible on-demand dual-core and triple-core lockstep grouping of computing cores with runtime split-lock capabilities. Further, we propose two recovery approaches, software-based and hardware-based, trading off performance and area overhead. Running at 430 MHz, our fault-tolerant cluster achieves up to 1160 MOPS on a matrix multiplication benchmark when configured in non-redundant mode and 617 and 414 MOPS in dual and triple mode, respectively. A software-based recovery in triple mode requires 363 clock cycles and occupies 0.612 mm2, representing a 1.3% area overhead over a non-redundant 12-core RISC-V cluster. As a high-performance alternative, a new hardware-based method provides rapid fault recovery in just 24 clock cycles and occupies 0.660 mm2, namely ~9.4% area overhead over the baseline non-redundant RISC-V cluster. The cluster is also enhanced with split-lock capabilities to enter one of the redundant modes with minimum performance loss, allowing execution of a mission-critical or a performance section, with <400 clock cycles overhead for entry and exit. The proposed system is the first to integrate these functionalities on an open-source RISC-V-based compute device, enabling finely tunable reliability vs. performance trade-offs.

eess.SY↗

arXiv2023

Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors

The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneous architectures that tightly co-integrate multiple specialized processor and memory chiplets. In such data-hungry multi-chip architectures, current Networks-in-Package (NiPs) may not be enough to cater to their heterogeneous and fast-changing communication demands. This position paper makes the case for wireless in-package nanonetworking as the enabler of efficient and versatile wired-wireless interconnect fabrics for massive heterogeneous processors. To that end, the use of graphene-based antennas and transceivers with unique frequency-beam reconfigurability in the terahertz band is proposed. The feasibility of such a nanonetworking vision and the main research challenges towards its realization are analyzed from the technological, communications, and computer architecture perspectives.

cs.ET↗

arXiv2023

Scalable Hierarchical Instruction Cache for Ultra-Low-Power Processors Clusters

High Performance and Energy Efficiency are critical requirements for Internet of Things (IoT) end-nodes. Exploiting tightly-coupled clusters of programmable processors (CMPs) has recently emerged as a suitable solution to address this challenge. One of the main bottlenecks limiting the performance and energy efficiency of these systems is the instruction cache architecture due to its criticality in terms of timing (i.e., maximum operating frequency), bandwidth, and power. We propose a hierarchical instruction cache tailored to ultra-low-power tightly-coupled processor clusters where a relatively large cache (L1.5) is shared by L1 private caches through a two-cycle latency interconnect. To address the performance loss caused by the L1 capacity misses, we introduce a next-line prefetcher with cache probe filtering (CPF) from L1 to L1.5. We optimize the core instruction fetch (IF) stage by removing the critical core-to-L1 combinational path. We present a detailed comparison of instruction cache architectures' performance and energy efficiency for parallel ultra-low-power (ULP) clusters. Focusing on the implementation, our two-level instruction cache provides better scalability than existing shared caches, delivering up to 20\% higher operating frequency. On average, the proposed two-level cache improves maximum performance by up to 17\% compared to the state-of-the-art while delivering similar energy efficiency for most relevant applications.

cs.AR↗

arXiv2023

CVA6 RISC-V Virtualization: Architecture, Microarchitecture, and Design Space Exploration

Virtualization is a key technology used in a wide range of applications, from cloud computing to embedded systems. Over the last few years, mainstream computer architectures were extended with hardware virtualization support, giving rise to a set of virtualization technologies (e.g., Intel VT, Arm VE) that are now proliferating in modern processors and SoCs. In this article, we describe our work on hardware virtualization support in the RISC-V CVA6 core. Our contribution is multifold and encompasses architecture, microarchitecture, and design space exploration. In particular, we highlight the design of a set of microarchitectural enhancements (i.e., G-Stage Translation Lookaside Buffer (GTLB), L2 TLB) to alleviate the virtualization performance overhead. We also perform a Design Space Exploration (DSE) and accompanying post-layout simulations (based on 22nm FDX technology) to assess Performance, Power ,and Area (PPA). Further, we map design variants on an FPGA platform (Genesys 2) to assess the functional performance-area trade-off. Based on the DSE, we select an optimal design point for the CVA6 with hardware virtualization support. For this optimal hardware configuration, we collected functional performance results by running the MiBench benchmark on Linux atop Bao hypervisor for a single-core configuration. We observed a performance speedup of up to 16% (approx. 12.5% on average) compared with virtualization-aware non-optimized design at the minimal cost of 0.78% in area and 0.33% in power. Finally, all work described in this article is publicly available and open-sourced for the community to further evaluate additional design configurations and software stacks.

cs.AR↗

arXiv2023

PATRONoC: Parallel AXI Transport Reducing Overhead for Networks-on-Chip targeting Multi-Accelerator DNN Platforms at the Edge

Emerging deep neural network (DNN) applications require high-performance multi-core hardware acceleration with large data bursts. Classical network-on-chips (NoCs) use serial packet-based protocols suffering from significant protocol translation overheads towards the endpoints. This paper proposes PATRONoC, an open-source fully AXI-compliant NoC fabric to better address the specific needs of multi-core DNN computing platforms. Evaluation of PATRONoC in a 2D-mesh topology shows 34% higher area efficiency compared to a state-of-the-art classical NoC at 1 GHz. PATRONoC's throughput outperforms a baseline NoC by 2-8X on uniform random traffic and provides a high aggregated throughput of up to 350 GiB/s on synthetic and DNN workload traffic.

cs.AR↗