Fault injection attacks are a threat to all digital systems, especially to the ones conducting security sensitive operations. Recently, the strategy of observing the instruction flow to detect attacks has gained popularity. In this paper, we provide a comparative study between three hardware-based techniques (i.e., recurrent neural network (RNN), content addressable memory (CAM), and Bloom filter (BF)) that detect fault attacks against software RSA decryption. After conducting experiments with various fault models, we observed that the CAM provides the best detection rate, the RNN provides the most software/application flexibility, and the BF is a middle ground between the two. Regardless, all of them exhibit robustness against faults targeted at them, and obtain a very high detection rate when faults change instructions altogether. This affirms the validity of monitoring the integrity of the instruction flow as a strong countermeasure against any type of fault attack.@en

Many modern FPGA-based soft-processor designs must include dedicated hardware modules to satisfy the requirements of a wide range of applications. Not seldom they all do not fit in the FPGA target, so their functionalities must be mapped into the much slower software domain. However, many complex soft-core processors usually underuse the available Block RAMs (BRAMs) when comparing to LUTs and registers. By taking advantage of this fact, we propose a generic low-cost BRAM-based function reuse mechanism (the BRAM-FR) that can be easily configured for precise or approximate modes to accelerate execution. The BRAM-FR was implemented in HDL and coupled to a configurable 4-issue VLIW processor. It was used to optimize different applications that use a soft-float library to emulate a Floating-Point Unit (FPU), and an image processing filter that tolerates a certain level of error. We show that our technique can accelerate the former by 1.23x and the latter by 1.52x, with a Reuse Table that fits in the BRAMs (that would otherwise be idle) of five tested FPGA targets with a marginal increase in the number of slice registers and LUTs.@en

Many modern FPGA-based soft-processor designs must include dedicated hardware modules to satisfy the requirements of a wide range of applications. Not seldom they all do not fit in the FPGA target, so their functionalities must be mapped into the much slower software domain. However, many complex soft-core processors usually underuse the available Block RAMs (BRAMs) when comparing to LUTs and registers. By taking advantage of this fact, we propose a generic low-cost BRAM-based function reuse mechanism (the BRAM-FR) that can be easily configured for precise or approximate modes to accelerate execution. The BRAM-FR was implemented in HDL and coupled to a configurable 4-issue VLIW processor. It was used to optimize different applications that use a soft-float library to emulate a Floating-Point Unit (FPU), and an image processing filter that tolerates a certain level of error. We show that our technique can accelerate the former by 1.23x and the latter by 1.52x, with a Reuse Table that fits in the BRAMs (that would otherwise be idle) of five tested FPGA targets with a marginal increase in the number of slice registers and LUTs.@en

Many modern FPGA-based soft-processor designs must include dedicated hardware modules to satisfy the requirements of a wide range of applications. Not seldom they all do not fit in the FPGA target, so their functionalities must be mapped into the much slower software domain. However, many complex soft-core processors usually underuse the available Block RAMs (BRAMs) when comparing to LUTs and registers. By taking advantage of this fact, we propose a generic low-cost BRAM-based function reuse mechanism (the BRAM-FR) that can be easily configured for precise or approximate modes to accelerate execution. The BRAM-FR was implemented in HDL and coupled to a configurable 4-issue VLIW processor. It was used to optimize different applications that use a soft-float library to emulate a Floating-Point Unit (FPU), and an image processing filter that tolerates a certain level of error. We show that our technique can accelerate the former by 1.23x and the latter by 1.52x, with a Reuse Table that fits in the BRAMs (that would otherwise be idle) of five tested FPGA targets with a marginal increase in the number of slice registers and LUTs.@en