  A Retrieval-Augmented Language Model (RALM) augments a generative language
model by retrieving context-specific knowledge from an external database. This
strategy facilitates impressive text generation quality even with smaller
models, thus reducing orders of magnitude of computational demands. However,
RALMs introduce unique system design challenges due to (a) the diverse workload
characteristics between LM inference and retrieval and (b) the various system
requirements and bottlenecks for different RALM configurations such as model
sizes, database sizes, and retrieval frequencies. We propose Chameleon, a
heterogeneous accelerator system that integrates both LM and retrieval
accelerators in a disaggregated architecture. The heterogeneity ensures
efficient acceleration of both LM inference and retrieval, while the
accelerator disaggregation enables the system to independently scale both types
of accelerators to fulfill diverse RALM requirements. Our Chameleon prototype
implements retrieval accelerators on FPGAs and assigns LM inference to GPUs,
with a CPU server orchestrating these accelerators over the network. Compared
to CPU-based and CPU-GPU vector search systems, Chameleon achieves up to 23.72x
speedup and 26.2x energy efficiency. Evaluated on various RALMs, Chameleon
exhibits up to 2.16x reduction in latency and 3.18x speedup in throughput
compared to the hybrid CPU-GPU architecture. These promising results pave the
way for bringing accelerator heterogeneity and disaggregation into future RALM
systems.
