Babak Falsafi

Adjunct Professor – ECE, CS

Department	Electrical and Computer Engineering
Email	babak.falsafi@epfl.ch
Website	http://www.ece.cmu.edu/~babak/

Research Interests

Prof. Falsafi's research group, Impetus, focuses on the design, evaluation, and implementation of computer systems with emphasis on processor and memory architecture. Current projects include:

Spatio-Temporal Memory Streaming (STEMS)

While memory capacity in recent years has increased commensurately with processor speeds, memory speeds have primarily lagged behind. The brute-force approach of building a hierarchy of storage elements — i.e., caches — trading off size for speed at every level has reached diminishing returns. A STEMS system extracts repetitive spatially- and temporally-correlated streams of instruction/data corresponding to computational fragments, and speculatively moves them among processors prior to use and away upon completion of reuse to bridge the processor/memory performance gap.

Architectural Support for Gigascale Integration

The continued scaling of CMOS processes and circuits will result in unprecedented low levels of chip reliability due to a number of error sources including but not limited to transient (soft) error — resulting from shrinking devices and increased vulnerability to cosmic radiation, gradual error — due to the increase in device performance variability, and wear-out error — due to degradation of devices over time. We are exploring the design space for architectural support to detect and recover from error in future gigascale CMOS designs in both single-chip and scalable computer systems.

Fast, accurate and flexible full-system simulation

Computer architects have long relied on software simulation to measure dynamic performance metrics (e.g. CPI) of a proposed design. Unfortunately, with the ever-growing size and complexity of modern hardware platforms, detailed software simulators have become prohibitively (e.g., > 1000000x) slower than their hardware counterparts. We are exploring simulation methodologies and frameworks for fast, yet accurate and flexible full-system simulation of large-scale computer systems. Our frameworks rely on statistical sampling to provide accuracy and speed, and componentization to provide flexibility.