High-performance hardware models for system simulation
Chris Eddington - Synopsys
12/11/2012 11:37 AM EST
Introduction
System simulators are becoming an increasingly important part of the FPGA and ASIC verification process, particularly for system-on-chips (SoCs) with performance-critical hardware accelerators and tightly-coupled embedded software. Cycle accuracy (CA) of the peripheral hardware is often a requirement or very desirable in many cases, especially if greater simulation performance over RTL simulation can be achieved. Some examples include:
- Detailed performance and utilization of system interconnect, based on the actual hardware implementation running with its embedded software.
- Implementation of low-level drivers and firmware, which require register maps and may rely on exact latency and flow control behavior of the peripheral.
- Software optimization, which can be particularly important for algorithm hardware accelerators, codec development, as well as in cases where hardware and software are tightly coupled and there is a critical overall performance goal in latency, throughput, etc. In such scenarios, estimates by ISS and TLM can be off by a factor of three, resulting either in wasted silicon or chips that cannot meet their required performance.
To read the full article, click here
Related Semiconductor IP
- ReRAM NVM in DB HiTek 130nm BCD
- UFS 5.0 Host Controller IP
- PDM Receiver/PDM-to-PCM Converter
- Voltage and Temperature Sensor with integrated ADC - GlobalFoundries® 22FDX®
- 8MHz / 40MHz Pierce Oscillator - X-FAB XT018-0.18µm
Related Articles
- Reusable Device Simulation Models for Embedded System Virtual Platforms
- HW/SW Interface Generation Flow Based on Abstract Models of System Applications and Hardware Architectures
- CANDoSA: A Hardware Performance Counter-Based Intrusion Detection System for DoS Attacks on Automotive CAN bus
- RoMe: Row Granularity Access Memory System for Large Language Models
Latest Articles
- An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networks
- Enabling RISC-V Vector Code Generation in MLIR through Custom xDSL Lowerings
- A Scalable Open-Source QEC System with Sub-Microsecond Decoding-Feedback Latency
- SNAP-V: A RISC-V SoC with Configurable Neuromorphic Acceleration for Small-Scale Spiking Neural Networks
- An FPGA Implementation of Displacement Vector Search for Intra Pattern Copy in JPEG XS