(Tentative) Schedule

Week	Date	Topic	Reading assignments	Project, etc.
1	1/18 1/20	Introduction	None
2	1/25 1/27	Real-Time Systems	Scheduling algorithms for multiprogramming in a hard-real-time environment, JACM, 1973
3	2/1 2/3	Real-Time Systems	Applying new scheduling theory to static priority pre-emptive scheduling, Software Engineering Journal, 1993 [pdf]
4	2/8 2/10	Real-Time Systems EECS Seminar
5	2/15 2/17	CPS Applications	Certification Authorities Software Team (CAST). CAST-32A: Multi-core Processors, 2016 [pdf] Autoware on board: enabling autonomous vehicles with embedded systems, ICCPS, 2018
6	2/22 2/24	Intelligent CPS	(Optional) DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car, RTCSA, 2018 (CAD)2RL : Real Single-Image Flight without a Single Real Image (Bailey) F1/10: An Open-Source Autonomous Cyber-Physical Platform, arXiv preprint, 2019
7	3/1 3/3	Predictable OS	Taming Non-blocking Caches to Improve Isolation in Multicore Real-Time Systems, RTAS, 2016 [pdf] RT-Gang: Real-Time Gang Scheduling Framework for Safety-Critical Systems, RTAS, 2019 [pdf] (Optional) Denial-of-Service Attacks on Shared Cache in Multicore: Analysis and Prevention, RTAS, 2019 [pdf]
8	3/8 3/10	Predictable Hardware	Deterministic Memory Abstraction and Supporting Multicore System Architecture. ECRTS, 2018 [pdf] BRU: Bandwidth Regulation Unit for Real-Time Multicore Processors, RTAS, 2020 [pdf]
9	3/15 3/17	NO CLASS (spring break)	None
10	3/22 3/24	Predictable GPU/Accelerator	Generating and Exploiting Deep Learning Variants to Increase Heterogeneous Resource Utilization in the NVIDIA Xavier. ECRTS 2019. Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform. RTAS 2020
11	3/29 3/31	Real-Time AI	Dissecting the CUDA scheduling hierarchy: A Performance and Predictability Perspective, RTAS, 2020 Pipelined Data-Parallel CPU/GPU Scheduling for Multi-DNN Real-Time Inference, RTSS, 2019 “Re-thinking CNN Frameworks for Time-Sensitive Autonomous-Driving Applications: Addressing an Industrial Challenge” RTAS 2019 On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines. RTSS, 2020.
12	4/5 4/7	Real-time AI/TinyML	MCUNet: Tiny Deep Learning on IoT Devices, NeurIPS, 2020 FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices, SynSys, 2018
13	4/12 4/14	Safety and Security Final exam	A Simplex Architecture for Intelligent and Safe Unmanned Aerial Vehicles, RTCSA, 2016 SpectreGuard: An Efficient Data-centric Defense Mechanism against Spectre Attacks, DAC, 2019 [pdf]
14	4/19 4/21	Project	None
15	4/26 4/28	Project	None
16	5/3 5/5	NO CLASS Project Presentation	None

Suggested Papers

• B. Brandenburg, Liu and Layland and Linux: A Blueprint for “Proper” Real-Time Tasks, ACM SIGBED Blog, 2020
• C. Liu, J. Layland. “Scheduling algorithms for multiprogramming in a hard-real-time environment,” JACM, 1973
• The rate monotonic scheduling algorithm: exact characterization and average case behavior, RTSS, 1989
• N. Audsley, et al., "Applying new scheduling theory to static priority pre-emptive scheduling," Software Engineering Journal, 1993
• S. Vestal. Preemptive scheduling of multi-criticality systems with varying degrees of execution time assurance, RTSS, 2007
• Parallel Scheduling for Cyber-Physical Systems: Analysis and Case Study on a Self-Driving Car, ICCPS'13

CPS Applications

• DeepRacing AI: Agile Trajectory Synthesis for Autonomous Racing, IROS, 2020
• F1/10: An Open-Source Autonomous Cyber-Physical Platform, arXiv preprint, 2019
• Certification Authorities Software Team (CAST). CAST-32A: Multi-core Processors, 2016
• End to End Learning for Self-Driving Cars, arXiv, 2016
• DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car, RTCSA, 2018
• An Open Approach to Autonomous Vehicles. MICRO, 2015
• Autoware on board: enabling autonomous vehicles with embedded systems, ICCPS, 2018
• Collaborative Autonomous Driving: Vision and Challenges, MetroCAD, 2020
• Cyber-Physical Modeling of Implantable Cardiac Medical Devices, IEEE, 2012
• Autonomous driving in urban environments: Boss and the Urban Challenge, 2008
• Human-level control through deep reinforcement learning. Nature, 2015
• (CAD)2RL : Real Single-Image Flight without a Single Real Image

Real-time Memory

• Analysis of Memory-Contention in Heterogeneous COTS MPSoCs, ECRTS, 2020 (slides)
• Denial-of-Service Attacks on Shared Cache in Multicore: Analysis and Prevention, RTAS, 2019
• A Bandwidth Reservation Mechanism for AXI-Based Hardware Accelerators on FPGAs, ECRTS, 2019
• Maximum-Contention Control Unit ( MCCU ): Resource Access Count and Contention Time Enforcement, DATE, 2019
• Taming Non-blocking Caches to Improve Isolation in Multicore Real-Time Systems, RTAS, 2016
• BRU: Bandwidth Regulation Unit for Real-Time Multicore Processors, RTAS, 2020
• Deterministic Memory Abstraction and Supporting Multicore System Architecture. ECRTS, 2018
• On the Off-chip Memory Latency of Real-Time Systems: Is DDR DRAM Really the Best Option?. RTSS, 2018
• Bounding DRAM Interference in COTS Heterogeneous MPSoCs for Mixed Criticality Systems, EMSOFT, 2018
• Parallelism-Aware Memory Interference Delay Analysis for COTS Multicore Systems. ECRTS, 2015
• Improved DRAM Timing Bounds for Real-Time DRAM Controllers with Read/Write Bundling, RTSS, 2015
• Predator: a predictable sdram memory controller”. CODES+ISSS 2007.
• PRET DRAM Controller: Bank Privatization for Predictability and Temporal Isolation
• A Comprehensive Study of DRAM Controllers in Real-Time Systems, Danlu Guo, MS Thesis, University of Waterloo, 2016
• Real-Time Control of I/O COTS Peripherals for Embedded Systems, RTSS, 2009

Real-Time Multi/Manycore Architecture

• Predictable Programming on a Precision Timed Architecture, CASES, 2008
• Hardware Support for WCET Analysis of Hard Real-Time Multicore Systems, ISCA 2009
• Merasa: Multicore execution of hard real-time applications supporting analyzability, Micro, 2010.
• FlexPRET: A Processor Platform for Mixed-Criticality Systems, RTAS, 2014
• Design and Analysis of SIC: A Provably Timing-Predictable Pipelined Processor Core, RTSS, 2018

Real-Time GPU and Accelerators

• Generating and Exploiting Deep Learning Variants to Increase Heterogeneous Resource Utilization in the NVIDIA Xavier. ECRTS 2019.
• Timing of Autonomous Driving Software: Problem Analysis and Prospects for Future Solutions RTAS 2020
• Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform. RTAS 2020
• NeuOS: A Latency-Predictable Multi-Dimensional Optimization Framework for DNN-driven Autonomous Systems, ATC, 2020.
• Dissecting the CUDA scheduling hierarchy: A Performance and Predictability Perspective, RTAS, 2020
• S^3dnn: Supervised streaming and scheduling for gpu-accelerated real-time dnn workloads." RTAS 2018
• Fractional GPUs: Software-based Compute and Memory Bandwidth Reservation for GPUs, RTAS 2019
• Pipelined Data-Parallel CPU/GPU Scheduling for Multi-DNN Real-Time Inference, RTSS, 2019
• Deadline-based Scheduling for GPU with Preemption Support, RTSS, 2018
• Protecting Real-Time GPU Kernels on Integrated CPU-GPU SoC Platforms, ECRTS, 2018

Real-Time Operating Systems/Middleware

• Holistic Resource Allocation for Multicore Real-Time Systems, RTAS, 2019
• Time Protection: the Missing OS Abstraction, EeroSys, 2019
• RT-Gang: Real-Time Gang Scheduling Framework for Safety-Critical Systems, RTAS, 2019
• Virtual Gang based Scheduling of Parallel Real-Time Tasks, DATE, 2021
• Exploiting Simultaneous Multithreading in Priority-Driven Hard Real-Time Systems, ECRTS, 2020
• MemGuard: Memory Bandwidth Reservation System for Efficient Performance Isolation in Multi-core Platforms. RTAS, 2013
• PALLOC: DRAM Bank-Aware Memory Allocator for Performance Isolation on Multicore Platforms, RTAS, 2014
• Achieving Predictable Multicore Execution of Automotive Applications Using the LET Paradigm, RTAS, 2018
• BWLOCK: A Dynamic Memory Access Control Framework for Soft Real-Time Applications on Multicore Platforms, IEEE Transactions on Computers, 2017.
• A Real-Time Scratchpad-Centric OS for Multi-Core Embedded Systems, RTAS, 2016
• SCHEDULING AND LOCKING IN MULTIPROCESSOR REAL-TIME OPERATING SYSTEMS. Chapter 3.3. The Design and Implementation of LITMUSRT. Bj¨orn B. Brandenburg, PhD Thesis, 2011
• Automatic Latency Management for ROS 2: Benefits, Challenges, and Open Problems, RTAS 2021
• Response-Time Analysis of ROS 2 Processing Chains under Reservation-Based Scheduling, ECRTS 2019
• ROS: an open-source Robot Operating System, ICRAOSS'09 [pdf]
• Certifying Applications in a Multi-Core Environment: a New Approach Gains Success, SYSGO/AG

Real-time AI/TinyML

• Scheduling Real-time Deep Learning Services as Imprecise Computations, RTCSA 2020
• On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines. RTSS, 2020.
• “Real-Time Object Detection System with Multi-Path Neural Networks” RTAS 2020
• “SubFlow: A Dynamic Induced-Subgraph Strategy Toward Real-Time DNN Inference and Training”, RTAS 2020
• “Real-Time Object Detection System with Multi-Path Neural Networks” RTAS 2020
• “Re-thinking CNN Frameworks for Time-Sensitive Autonomous-Driving Applications: Addressing an Industrial Challenge” RTAS 2019
• “ApNet: Approximation-aware Real-Time Neural Network” RTSS 2018.
• DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework, SenSys, 2017
• MCUNet: Tiny Deep Learning on IoT Devices, NeurIPS, 2020
• PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units, HPCA, 2020

Fault Tolerance

• Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots, arXiv, 2019
• Towards Adaptive Fault Tolerance: From a Component-Based Approach to ROS [pdf]
• Application and System-Level Software Fault Tolerance Through Full System Restarts, ICCPS'2017
• A Simplex Architecture for Intelligent and Safe Unmanned Aerial Vehicles, RTCSA, 2016
• Reset-Based Recovery for Real-Time Cyber-Physical Systems with Temporal Safety Constraints. ETFA 2016
• SAFER: System-level Architecture for Failure Evasion in Real-time Applications. RTSS’12
• “The System-Level Simplex Architecture for Improved Real-Time Embedded System Safety,” RTAS 2009.
• L. Sha, “Using simplicity to control complexity, 2001

Security

• HERA: Hotpatching of Embedded Real-time Applications, NDSS, 2021
• "Revisiting RowHammer: An Experimental Analysis of Modern Devices and Mitigation Techniques", ISCA, 2020
• "Are We Susceptible to Rowhammer? An End-to-End Methodology for Cloud Providers", IEEE S&P, 2020
• "TRRespass: Exploiting the Many Sides of Target Row Refresh", IEEE S&P, 2020
• Comprehensive Experimental Analyses of Automotive Attack Surfaces, Usenix security, 2010
• Drone hack: Spoofing attack demonstration on a civilian unmanned aerial vehicle. GPS World, August 2012.
• ReSecure: A Restart-Based Security Protocol for Tightly Actuated Hard Real-Time Systems, CERTS'16
• Rocking Drones with Intentional Sound Noise on Gyroscopic Sensors, USENIX security’15
• Flipping Bits in Memory Without Accessing Them: An Experimental Study of DRAM Disturbance Errors. ISCA, 2014
• Drammer: Deterministic Rowhammer Attacks on Mobile Platforms, CCS, 2016 [blog]
• Meltdown: Reading Kernel Memory from User Space, USENIX Security, 2018
• Spectre Attacks: Exploiting Speculative Execution, IEEE Security and Privacy (S&P), 2019
• SpectreGuard: An Efficient Data-centric Defense Mechanism against Spectre Attacks, DAC, 2019 [pdf]
• On Subnormal Floating Point and Abnormal Timing. IEEE Security and Privacy, 2015
• RIDL: Rogue In-Flight Data Load, IEEE S&P, 2019
• Speculative Taint Tracking (STT): A Comprehensive Protection for Speculatively Accessed Data, ISCA, 2019

TinyML

• DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework, SenSys, 2017
• FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices, SynSys, 2018
• MCUNet: Tiny Deep Learning on IoT Devices, NeurIPS, 2020
• ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware, ICLR, 2019
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, ICLR, 2015
• SqueezeNet: AlexNet-Level Accuracy with 50x Fewer Parameters and < 0.5MB Model Size, arXiv, 2016.

Other papers/resources

• A Safe, Secure, and Predictable Software Architecture for Deep Learning in Safety-Critical Systems, IEEE Embedded Systems Letters, 2019
• System Performance Modelling of Heterogeneous HW Platforms: An Automated Driving Case Study, DSD, 2019
• F1/10 Autonomous Racing class at UVA