Final Program

2021 2021 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS)  
Quantum computing promises exponential speedups for an important class of problems. While quantum computers with few dozens of qubits have been demonstrated, these machines suffer from a high rate of gate errors. Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) mode of computing where the output of the machine can be erroneous. In this talk, I will discuss some of our recent work that aims to improve the reliability of NISQ computers by developing software techniques to
more » ... mitigate hardware errors. Our first work exploits the variability in the error rates of qubits to steer more operations towards qubits with lower error rates and avoid qubits that are error-prone. Our second work looks at executing different versions of the programs each crafted to cause diverse mistakes so that the machine becomes less vulnerable to correlated errors. Our third work looks at exploiting the state-dependent bias in measurement errors (state 1 is more error-prone than state 0) and dynamically flips the state of the qubit to perform the measurement in the stronger state. We perform our evaluations on real quantum machines from IBM and demonstrate significant improvement in the overall system reliability. Finally, I will also briefly discuss the hardware aspect of designing largescale quantum computers, including cryogenic processor and cryogenic memory system. Abstract: As the amount of sensitive information processed by computers constantly increases, there is a need to continue to harden the processors, and the whole computer systems. Among the possible threats, the variety of remote attacks are of importance since they do not require attacker to be physically near the target system, they only require that attacker and victim are executing on the same system, such as by being co-located on same server in a public cloud computing data center. At the same time, there is ever-expanding use of machine learning and other algorithms that process sensitive information in the cloud data centers. Both data, as well as the algorithms, e.g. the specific machine learning architectures or models, can be targets of attacks. This opens up the various algorithms to variety of hardware-rooted side and covert channel attacks, which continue to pose threat to our privacy and security. Meanwhile, considering only performance or security is not enough, and the processor designers need to be mindful of the power consumption and energy usage of their systems. In this talk, we will first cover various remote timing and power related information leaks to give background of Poster 11 Software management system of the PYNQ cluster Takumi Inage, Kazuei Hironaka, Kensuke Iizuka, Hideharu Amano (Keio Univ.) Poster 12 Abstract: In recent years, domain specific architectures are thriving. One main reason that fuels this trend is the prolific domain of machine learning. In this talk I will briefly survey some of the main approaches and a glimpse into theoretical aspects that underlie their suggested benefit. I will share some observations on present and future developments in the field and share my subjective view on about the possible implications on compute architectures. Abstract: The extraordinary market demand for large-scale machine learning
doi:10.1109/coolchips52128.2021.9410348 fatcat:rtgywenc4bd2lbixraustje6xa