This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of size two for a lower hardware solution while maintaining similar input-output characteristic to the algorithm. The blocked solution, here referred to as B2P algorithm, is obtained using graph theory and retiming methods. Verification approaches were exercised to show that prediction performances obtained from the FPP and B2P algorithms differ within one misprediction per thousand instructions using a known framework for branch prediction evaluation. For a chosen FPGA device, circuits generated from the B2P algorithm showed average area savings of over 25% against circuits for the FPP algorithm with similar time performances thus making the p...
Microarchitectural prediction based on neural learning has received increasing attention in recent y...
The branch predictor plays a crucial role in the achievement of effective performance in microproces...
This research shows that using an Artificial Neural Network as the hardware branch predictor of a su...
This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of...
This paper contributes to a dynamic branch predictor algorithm based on a perceptron in two directio...
This paper develops cycle-level FPGA circuits of an organization for a fast path-based neural branch...
Branch prediction has been extensively studied in the context of application specific custom logic (...
A conventional path-based neural predictor (PBNP) achieves very high prediction accuracy, but its ve...
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
An unaltered rearrangement of the original computation of a neural based predictor at the algorithmi...
Instructions pipelining is one of the most outstanding techniques used in improving processor speed;...
Recently, the discovery of memristor brought the promise of high density, low energy, and combined m...
Accurate branch prediction is essential for modern microprocessors in order to maintain high in-stru...
In this paper, we propose a Bayesian branch-prediction circuit, consisting of an instruction-feature...
We present a new method for branch prediction that encodes in the branch instruction a formula, chos...
Microarchitectural prediction based on neural learning has received increasing attention in recent y...
The branch predictor plays a crucial role in the achievement of effective performance in microproces...
This research shows that using an Artificial Neural Network as the hardware branch predictor of a su...
This paper formally derives a new path-based neural branch prediction algorithm (FPP) into blocks of...
This paper contributes to a dynamic branch predictor algorithm based on a perceptron in two directio...
This paper develops cycle-level FPGA circuits of an organization for a fast path-based neural branch...
Branch prediction has been extensively studied in the context of application specific custom logic (...
A conventional path-based neural predictor (PBNP) achieves very high prediction accuracy, but its ve...
This article presents a new and highly accurate method for branch prediction. The key idea is to use...
An unaltered rearrangement of the original computation of a neural based predictor at the algorithmi...
Instructions pipelining is one of the most outstanding techniques used in improving processor speed;...
Recently, the discovery of memristor brought the promise of high density, low energy, and combined m...
Accurate branch prediction is essential for modern microprocessors in order to maintain high in-stru...
In this paper, we propose a Bayesian branch-prediction circuit, consisting of an instruction-feature...
We present a new method for branch prediction that encodes in the branch instruction a formula, chos...
Microarchitectural prediction based on neural learning has received increasing attention in recent y...
The branch predictor plays a crucial role in the achievement of effective performance in microproces...
This research shows that using an Artificial Neural Network as the hardware branch predictor of a su...