Current trends in technology scaling foreshadow worsening transistor reliability as well as greater numbers of transistors in each system. The combination of these factors will soon make long-term product reliability extremely difficult in complex modern systems such as systems on a chip (SoC) and chip multiprocessor (CMP) designs, where even a single device failure can cause fatal system errors. Resiliency to device failure will be a necessary condition at future technology nodes. In this work, we present a network-onchip (NoC) routing algorithm to boost the robustness in interconnect networks, by reconfiguring them to avoid faulty components while maintaining connectivity and correct operation. This distributed algorithm can be implemented in hardware with less than 300 gates per network router. Experimental results over a broad range of 2D-mesh and 2D-torus networks demonstrate 99.99% reliability on average when 10% of the interconnect links have failed.

We present an algorithm for extracting a disjunctive decomposition

As silicon process technology scales deeper into the nanometer regime, hardware defects are becoming more common. Such defects are bound to hinder the correct operation of future processor systems, unless new online techniques become available to detect and to tolerate them while preserving the integrity of software applications running on the system. This paper proposes a new, software-based, defect detection and diagnosis technique. We introduce a novel set of instructions, called Access-Control Extension (ACE), that can access and control the microprocessor's internal state. Special firmware periodically suspends microprocessor execution and uses the ACE instructions to run directed tests on the hardware. When a hardware defect is present, these tests can diagnose and locate it, and then activate system repair through resource reconfiguration. The software nature of our framework makes it flexible: testing techniques can be modified/upgraded in the field to trade off performance with reliability without requiring any change to the hardware.We evaluated our technique on a commercial chip-multiprocessor based on Sun's Niagara and found that it can provide very high coverage, with 99.22% of all silicon defects detected. Moreover, our results show that the average performance overhead of softwarebased testing is only 5.5%. Based on a detailed RTL-level implementation of our technique, we find its area overhead to be quite modest, with only a 5.8% increase in total chip area.

For any computing system to be secure, both hardware and software have to be trusted. If the hardware layer in a secure system is compromised, not only it would be possible to extract secret information about the software, but it would also be extremely hard for the software to detect that an attack is underway. In this work we detail a complete end-to-end fault-attack on a microprocessor system and practically demonstrate how hardware vulnerabilities can be exploited to target secure systems. We developed a theoretical attack to the RSA signature algorithm, and we realized it in practice against an FPGA implementation of the system under attack. To perpetrate the attack, we inject transient faults in the target machine by regulating the voltage supply of the system. Thus, our attack does not require access to the victim system's internal components, but simply proximity to it.The paper makes three important contributions: first, we develop a systematic fault-based attack on the modular exponentiation algorithm for RSA. Second, we expose and exploit a severe flaw on the implementation of the RSA signature algorithm on OpenSSL, a widely used package for SSL encryption and authentication. Third, we report on the first physical demonstration of a fault-based security attack of a complete microprocessor system running unmodified production software: we attack the original OpenSSL authentication library running on a SPARC Linux system implemented on FPGA, and extract the system's 1024-bit RSA private key in approximately 100 hours.

Extreme technology scaling in silicon devices drastically affects reliability, particularly because of runtime failures induced by transistor wearout. Current online testing mechanisms focus on testing all components in a microprocessor, including hardware that has not been exercised, and thus have high performance penalties.We propose a hybrid hardware/software online testing solution where components that are heavily utilized by the software application are tested more thoroughly and frequently. Thus, our online testing approach focuses on the processor units that affect application correctness the most, and it achieves high coverage while incurring minimal performance overhead. We also introduce a new metric, Application-Aware Fault Coverage, measuring a test's capability to detect faults that might have corrupted the state or the output of an application. Test coverage is further improved through the insertion of observation points that augment the coverage of the testing system. By evaluating our technique on a Sun OpenSPARC T1, we show that our solution maintains high Application-Aware Fault Coverage while reducing the performance overhead of online testing by more than a factor of 2 when compared to solutions oblivious to application's behavior. Specifically, we found that our solution can achieve 95% fault coverage while maintaining a minimal performance overhead (1.3%) and area impact (0.4%).