Graph pattern matching (e.g., finding all cycles and cliques) has become an important component in many critical domains such as social networks, biology, and cyber-security. This development motivated research to develop faster algorithms that target graph pattern matching. In recent years, the database community has shown that mapping graph pattern matching problems to a new class of relational join algorithms provides an efficient framework for computing these problems. In this paper, we argue that this new class of relational join algorithms is highly amenable to specialized hardware acceleration thanks to two fundamental properties: improved locality and inherent concurrency. The improved locality is a result of the provably bound number of intermediate results these algorithms generate, which results in smaller working sets. In addition, their inherent concurrency can be leveraged for effective hardware acceleration and hiding memory latency. We demonstrate the hardware amenability of this new class of algorithms by introducing TrieJax, a hardware accelerator for graph pattern matching. The TrieJax design leverages the improved locality and high concurrency properties to dramatically accelerate graph pattern matching, and can be tightly integrated into existing manycore processors. We evaluate TrieJax on a set standard graph pattern matching queries and datasets. Our evaluation shows that TrieJax outperforms recently proposed hardware accelerators for graph and database processing that do not employ the new class of algorithms by 7-63x on average (up to 539x), while consuming 15-179x less energy (up to 1750x). systems that do incorporate modern relational join algorithms by 9-20x on average (up to 45x), while consuming 59-110x less energy (up to 372x).