Multi-processor system-on-chip 2: Applications

Cover
Half-Title Page
Dedication
Title Page
Copyright Page
Contents
Foreword
Acknowledgments
PART 1: MPSoC for Telecom
	1 From Challenges to Hardware Requirements for Wireless Communications Reaching 6G
		1.1. Introduction
		1.2. Breadth of workloads
			1.2.1. Vision, trends and applications
			1.2.2. Standard specifications
			1.2.3. Outcome of workloads
		1.3. GFDM algorithm breakdown
			1.3.1. Equation
			1.3.2. Dataflow processing graph and matrix representation
			1.3.3. Pseudo-code
		1.4. Algorithm precision requirements and considerations
		1.5. Implementation
			1.5.1. Implementation considerations
			1.5.2. Design space exploration
			1.5.3. Measurements for low-end and high-end use cases
		1.6. Conclusion
		1.7. Acknowledgments
		1.8. References
	2 Towards Tbit/s Wireless Communication Baseband Processing: When Shannon meets Moore
		2.1. Introduction
		2.2. Role of microelectronics
		2.3. Towards 1 Tbit/s throughput decoders
			2.3.1. Turbo decoder
			2.3.2. LDPC decoder
			2.3.3. Polar decoder
		2.4. Conclusion
		2.5. Acknowledgments
		2.6. References
PART 2: Application-specific MPSoC Architectures
	3 Automation for Industry 4.0 by using Secure LoRaWAN Edge Gateways
		3.1. Introduction
		3.2. Security in IIoT
		3.3. LoRaWAN security in IIoT
		3.4. Threat model
			3.4.1. LoRaWAN attack model
			3.4.2. IIoT node attack model
		3.5. Trusted boot chain with STM32MP1
			3.5.1. Trust base of node
			3.5.2. Trusted firmware in STM32MP1
			3.5.3. Trusted execution environments and OP-TEE
			3.5.4. OP-TEE scheduling considerations
			3.5.5. OP-TEE memory management
			3.5.6. OP-TEE client API
			3.5.7. TEE internal core API
			3.5.8. Root and chain of trust
			3.5.9. Hardware unique key
			3.5.10. Secure clock
			3.5.11. Cryptographic operations
		3.6. LoRaWAN gateway with STM32MP1
		3.7. Discussion and future scope
		3.8. Acknowledgments
		3.9. References
	4 Accelerating Virtualized Distributed NVMe Storage in Hardware
		4.1. Introduction
			4.1.1. Virtualization and traditional hypervisors
			4.1.2. Hyperconverged versus disaggregated cloud architectures
			4.1.3. NVMe flash storage
		4.2. Motivation: NVMe storage for the cloud
			4.2.1. Motivation for a new hypervisor
			4.2.2. Motivation for accelerating disaggregated storage
		4.3. Design
			4.3.1. Optimizing the hypervisor I/O operations
			4.3.2. Design of accelerated disaggregated storage
		4.4. Implementation
			4.4.1. The NexVisor platform
			4.4.2. Accelerated disaggregated storage
		4.5. Results
			4.5.1. Sequential reads
			4.5.2. Sequential writes
			4.5.3. Sequential reads on one NVMe drive
			4.5.4. Network performance
		4.6. Conclusion
		4.7. References
	5 Modular and Open Platform for Future Automotive Computing Environment
		5.1. Introduction
		5.2. Outline of this approach
			5.2.1. Centralized computation, distributed data
			5.2.2. Modularity and heterogeneity
			5.2.3. Tools for specification, configuration and integration
		5.3. Results
			5.3.1. Hardware platform
			5.3.2. FACE SW architecture
			5.3.3. FACE Tool Suite
		5.4. Use case
			5.4.1. Adaptive braking system
		5.5. Conclusion
		5.6. References
	6 Post-Moore Datacenter Server Architecture
		6.1. Introduction
		6.2. Background: today’s blades are from the desktops of the 1980s
		6.3. Memory-centric server design
		6.4. Data management accelerators
		6.5. Integrated network controllers
		6.6. References
PART 3: Architecture Examples and Tools for MPSoC
	7 SESAM: A Comprehensive Framework for Cyber–Physical System Prototyping
		7.1. Introduction
		7.2. An overview of the SESAM platform
			7.2.1. Multi-abstraction system prototyping
			7.2.2. Assessing extra-functional system properties
		7.3. VPSim: fast and easy virtual prototyping
			7.3.1. Writing peripherals in Python
			7.3.2. The ModelProvider interface
			7.3.3. QEMU support
			7.3.4. Online simulation monitoring
			7.3.5. Acceleration methods
		7.4. Hybrid prototyping
			7.4.1. Co-simulation mode
			7.4.2. Co-emulation mode
			7.4.3. Runtime performance analysis and debugging features
		7.5. FMI for co-simulation
			7.5.1. Functional mock-up interface
			7.5.2. VPSim integration in FMI co-simulation
		7.6. Conclusion
		7.7. References
	8 StaccatoLab: A Programming and Execution Model for Large-scale Dataflow Computing
		8.1. Introduction
		8.2. Static dataflow
			8.2.1. Synchronous dataflow
			8.2.2. Cyclo-static dataflow
			8.2.3. Dataflow graph transformations
		8.3. Dynamic dataflow
			8.3.1. Data-dependent dataflow
			8.3.2. Non-determinate dataflow
		8.4. Dataflow execution models
			8.4.1. A brief review of dataflow theory
			8.4.2. The StaccatoLab execution model
		8.5. StaccatoLab
			8.5.1. Dataflow graph description and analysis
			8.5.2. Verilog synthesis
		8.6. Large-scale dataflow computing?
			8.6.1. What kind of applications?
			8.6.2. Why effective?
			8.6.3. Why efficient?
		8.7. Acknowledgments
		8.8. References
	9 Smart Cameras and MPSoCs
		9.1. Introduction
		9.2. Early VLSI video processors
		9.3. Video signal processors
		9.4. Accelerators
		9.5. From VSP to MPSoC
		9.6. Graphics processing units
		9.7. Neural networks and tensor processing units
		9.8. Conclusion
		9.9. References
	10. Software Compilation and Optimization Techniques for Heterogeneous Multi-core Platforms
		10.1. Introduction
		10.2. Dataflow modeling
			10.2.1. General concepts
			10.2.2. Process networks
			10.2.3. C for process networks
		10.3. Source-to-source-based compiler infrastructure
			10.3.1. Design rationale
			10.3.2. Implementation strategy
		10.4. Software distribution
			10.4.1. KPN analysis
			10.4.2. Static KPN mapping
			10.4.3. Hybrid KPN mapping
		10.5. Results
			10.5.1. Applications and experiences
			10.5.2. Retargetability
		10.6. Conclusion
		10.7. References
List of Authors
Author Biographies
Index
EULA