There is an evolutionary progression of Field Programmable Gate Arrays (FPGAs)
toward more complex and high power density architectures such as Systems-on-
Chip (SoC) and Adaptive Compute Acceleration Platforms (ACAP). Primarily, this is
attributable to the continual transistor miniaturisation and more innovative and
efficient IC manufacturing processes. Concurrently, degradation mechanism of Bias
Temperature Instability (BTI) has become more pronounced with respect to its
ageing impact. It could weaken the reliability of VLSI devices, FPGAs in particular
due to their run-time reconfigurability. At the same time, vulnerability of FPGAs to
device-level attacks in the increasing cyber and hardware threat environment is also
quadrupling as the susceptible reliability realm opens door for the rogue elements to
intervene. Insertion of highly stealthy and malicious circuitry, called hardware
Trojans, in FPGAs is one of such malicious interventions. On the one hand where
such attacks/interventions adversely affect the security ambit of these devices, they
also undermine their reliability substantially. Hitherto, the security and reliability are
treated as two separate entities impacting the FPGA health. This has resulted in
fragmented solutions that do not reflect the true state of the FPGA operational and
functional readiness, thereby making them even more prone to hardware attacks.
The recent episodes of Spectre and Meltdown vulnerabilities are some of the key
examples. This research addresses these concerns by adopting an integrated
approach and investigating the FPGA security and reliability as two inter-dependent
entities with an additional dimension of health estimation/ prognostics. The design
and implementation of a small footprint frequency and threshold voltage-shift
detection sensor, a novel hardware Trojan, and an online transistor dynamic scaling
circuitry present a viable FPGA security scheme that helps build a strong
microarchitectural level defence against unscrupulous hardware attacks. Augmented
with an efficient Kernel-based learning technique for FPGA health
estimation/prognostics, the optimal integrated solution proves to be more
dependable and trustworthy than the prevalent disjointed approach.Samie, Mohammad (Associate)PhD in Transport System
